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A comprehensive step-by-step guide
Programming in
Scala
Third Edition
Prepared for Library Genesis
Updated for Scala 2.12
artima
Martin Odersky
Lex Spoon
Bill Venners
Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
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
Prepared for Library Genesis
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
Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
iii
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
Prepared for Library Genesis
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 objectoriented 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
Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
iv
I bought an early electronic version of the Programming in Scala book,
by Odersky, Spoon, and Venners, and I was immediately a fan. In addition
to the fact that it contains the most comprehensive information about the
language, there are a few key features of the electronic format that impressed
me. I have never seen links used as well in a PDF, not just for bookmarks,
but also providing active links from the table of contents and index. I don’t
know why more authors don’t use this feature, because it’s really a joy for
the reader. Another feature which I was impressed with was links to the
forums (“Discuss”) and a way to send comments (“Suggest”) to the authors
via email. The comments feature by itself isn’t all that uncommon, but the
simple inclusion of a page number in what is generated to send to the authors
is valuable for both the authors and readers. I contributed more comments
than I would have if the process would have been more arduous.
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
Prepared for Library Genesis
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 wellwritten 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.
Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
v
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
Prepared for Library Genesis
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
Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
Programming in Scala
Prepared for Library Genesis
Third Edition
Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
Programming in Scala
Third Edition
Prepared for Library Genesis
Martin Odersky, Lex Spoon, Bill Venners
artima
A RTIMA P RESS
WALNUT C REEK , C ALIFORNIA
Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
viii
Programming in Scala
Third Edition
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.
Artima Press is an imprint of Artima, Inc.
P.O. Box 305, Walnut Creek, California 94597
Copyright © 2007-2016 Martin Odersky, Lex Spoon, and Bill Venners.
All rights reserved.
First edition published as PrePrint™ eBook 2007
First edition published 2008
Second edition published as PrePrint™ eBook 2010
Second edition published 2010
Third edition published as PrePrint™ eBook 2016
Third edition published 2016
Build date of this impression April 6, 2016
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.
Prepared for Library Genesis
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.
Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
to Nastaran - M.O.
to Fay - L.S.
Prepared for Library Genesis
to Siew - B.V.
Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
Prepared for Library Genesis
Overview
Contents
List of Figures
List of Tables
List of Listings
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. Scala’s Hierarchy
12. Traits
13. Packages and Imports
14. Assertions and Tests
15. Case Classes and Pattern Matching
16. Working with Lists
17. Working with Other Collections
18. Mutable Objects
19. Type Parameterization
20. Abstract Members
21. Implicit Conversions and Parameters
22. Implementing Lists
23. For Expressions Revisited
24. Collections in Depth
25. The Architecture of Scala Collections
26. Extractors
27. Annotations
28. Working with XML
29. Modular Programming Using Objects
30. Object Equality
31. Combining Scala and Java
32. Futures and Concurrency
33. Combinator Parsing
34. GUI Programming
35. The SCells Spreadsheet
A. Scala Scripts on Unix and Windows
Glossary
Bibliography
About the Authors
Index
Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
xi
xxi
xxiii
xxv
xxxii
xxxiv
xxxvii
45
64
77
99
112
135
155
180
203
218
246
257
276
294
306
340
373
395
418
443
474
499
512
528
597
621
637
645
659
674
698
714
736
765
777
802
803
819
822
823
Contents
Contents
xi
List of Figures
xxi
List of Tables
xxiii
List of Listings
Foreword
xxxii
Acknowledgments
xxxiv
Introduction
xxxvii
1
2
Prepared for Library Genesis
xxv
A Scalable Language
1.1 A language that grows on you
1.2 What makes Scala scalable? .
1.3 Why Scala? . . . . . . . . . .
1.4 Scala’s roots . . . . . . . . .
1.5 Conclusion . . . . . . . . . .
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45
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51
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61
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First Steps in Scala
Step 1. Learn to use the Scala interpreter .
Step 2. Define some variables . . . . . .
Step 3. Define some functions . . . . . .
Step 4. Write some Scala scripts . . . . .
Step 5. Loop with while; decide with if
Step 6. Iterate with foreach and for . .
Conclusion . . . . . . . . . . . . . . . .
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Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
Contents
3
4
5
Prepared for Library Genesis
6
xii
Next Steps in Scala
Step 7. Parameterize arrays with types . . . .
Step 8. Use lists . . . . . . . . . . . . . . . .
Step 9. Use tuples . . . . . . . . . . . . . . .
Step 10. Use sets and maps . . . . . . . . . . .
Step 11. Learn to recognize the functional style
Step 12. Read lines from a file . . . . . . . . .
Conclusion . . . . . . . . . . . . . . . . . . .
Classes and Objects
4.1 Classes, fields, and methods
4.2 Semicolon inference . . . .
4.3 Singleton objects . . . . . .
4.4 A Scala application . . . .
4.5 The App trait . . . . . . . .
4.6 Conclusion . . . . . . . . .
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Basic Types and Operations
5.1 Some basic types . . . . . . . . . . .
5.2 Literals . . . . . . . . . . . . . . . .
5.3 String interpolation . . . . . . . . . .
5.4 Operators are methods . . . . . . . .
5.5 Arithmetic operations . . . . . . . .
5.6 Relational and logical operations . .
5.7 Bitwise operations . . . . . . . . . .
5.8 Object equality . . . . . . . . . . . .
5.9 Operator precedence and associativity
5.10 Rich wrappers . . . . . . . . . . . .
5.11 Conclusion . . . . . . . . . . . . . .
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133
133
Functional Objects
6.1 A specification for class Rational . .
6.2 Constructing a Rational . . . . . . .
6.3 Reimplementing the toString method
6.4 Checking preconditions . . . . . . . .
6.5 Adding fields . . . . . . . . . . . . . .
6.6 Self references . . . . . . . . . . . . .
6.7 Auxiliary constructors . . . . . . . . .
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Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
Contents
6.8
6.9
6.10
6.11
6.12
6.13
6.14
7
8
Prepared for Library Genesis
9
Private fields and methods
Defining operators . . . .
Identifiers in Scala . . . .
Method overloading . . .
Implicit conversions . . .
A word of caution . . . .
Conclusion . . . . . . . .
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xiii
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153
Built-in Control Structures
7.1 If expressions . . . . . . . . . . . . . .
7.2 While loops . . . . . . . . . . . . . . .
7.3 For expressions . . . . . . . . . . . . . .
7.4 Exception handling with try expressions
7.5 Match expressions . . . . . . . . . . . .
7.6 Living without break and continue . .
7.7 Variable scope . . . . . . . . . . . . . .
7.8 Refactoring imperative-style code . . . .
7.9 Conclusion . . . . . . . . . . . . . . . .
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Functions and Closures
8.1 Methods . . . . . . . . . . . .
8.2 Local functions . . . . . . . . .
8.3 First-class functions . . . . . .
8.4 Short forms of function literals
8.5 Placeholder syntax . . . . . . .
8.6 Partially applied functions . . .
8.7 Closures . . . . . . . . . . . .
8.8 Special function call forms . . .
8.9 Tail recursion . . . . . . . . . .
8.10 Conclusion . . . . . . . . . . .
Control Abstraction
9.1 Reducing code duplication . .
9.2 Simplifying client code . . .
9.3 Currying . . . . . . . . . . .
9.4 Writing new control structures
9.5 By-name parameters . . . . .
9.6 Conclusion . . . . . . . . . .
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Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
Prepared for Library Genesis
Contents
xiv
10 Composition and Inheritance
10.1 A two-dimensional layout library . . . . . .
10.2 Abstract classes . . . . . . . . . . . . . . .
10.3 Defining parameterless methods . . . . . . .
10.4 Extending classes . . . . . . . . . . . . . .
10.5 Overriding methods and fields . . . . . . . .
10.6 Defining parametric fields . . . . . . . . . .
10.7 Invoking superclass constructors . . . . . . .
10.8 Using override modifiers . . . . . . . . . .
10.9 Polymorphism and dynamic binding . . . .
10.10 Declaring final members . . . . . . . . . . .
10.11 Using composition and inheritance . . . . .
10.12 Implementing above, beside, and toString
10.13 Defining a factory object . . . . . . . . . . .
10.14 Heighten and widen . . . . . . . . . . . . .
10.15 Putting it all together . . . . . . . . . . . . .
10.16 Conclusion . . . . . . . . . . . . . . . . . .
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218
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225
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240
244
245
11 Scala’s Hierarchy
11.1 Scala’s class hierarchy . . . . . .
11.2 How primitives are implemented
11.3 Bottom types . . . . . . . . . . .
11.4 Defining your own value classes .
11.5 Conclusion . . . . . . . . . . . .
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246
246
250
252
253
256
12 Traits
12.1 How traits work . . . . . . . . .
12.2 Thin versus rich interfaces . . . .
12.3 Example: Rectangular objects . .
12.4 The Ordered trait . . . . . . . .
12.5 Traits as stackable modifications .
12.6 Why not multiple inheritance? . .
12.7 To trait or not to trait? . . . . . .
12.8 Conclusion . . . . . . . . . . . .
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257
257
260
261
264
266
270
274
275
13 Packages and Imports
13.1 Putting code in packages . . . . . . . . . . . . . . . . .
13.2 Concise access to related code . . . . . . . . . . . . . .
276
276
277
Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
Contents
Prepared for Library Genesis
13.3
13.4
13.5
13.6
13.7
Imports . . . . .
Implicit imports
Access modifiers
Package objects
Conclusion . . .
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281
285
286
291
293
14 Assertions and Tests
14.1 Assertions . . . . . . . . . .
14.2 Testing in Scala . . . . . . .
14.3 Informative failure reports . .
14.4 Tests as specifications . . . .
14.5 Property-based testing . . . .
14.6 Organizing and running tests
14.7 Conclusion . . . . . . . . . .
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294
294
296
297
299
302
304
305
15 Case Classes and Pattern Matching
15.1 A simple example . . . . . . .
15.2 Kinds of patterns . . . . . . . .
15.3 Pattern guards . . . . . . . . .
15.4 Pattern overlaps . . . . . . . .
15.5 Sealed classes . . . . . . . . .
15.6 The Option type . . . . . . . .
15.7 Patterns everywhere . . . . . .
15.8 A larger example . . . . . . . .
15.9 Conclusion . . . . . . . . . . .
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306
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331
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340
340
341
341
342
343
345
357
365
367
368
372
16 Working with Lists
16.1 List literals . . . . . . . . . . . . . . . . . . . .
16.2 The List type . . . . . . . . . . . . . . . . . .
16.3 Constructing lists . . . . . . . . . . . . . . . . .
16.4 Basic operations on lists . . . . . . . . . . . . .
16.5 List patterns . . . . . . . . . . . . . . . . . . .
16.6 First-order methods on class List . . . . . . . .
16.7 Higher-order methods on class List . . . . . .
16.8 Methods of the List object . . . . . . . . . . .
16.9 Processing multiple lists together . . . . . . . .
16.10 Understanding Scala’s type inference algorithm .
16.11 Conclusion . . . . . . . . . . . . . . . . . . . .
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Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
Prepared for Library Genesis
Contents
xvi
17 Working with Other Collections
17.1 Sequences . . . . . . . . . . . . . . . . . . . .
17.2 Sets and maps . . . . . . . . . . . . . . . . . .
17.3 Selecting mutable versus immutable collections
17.4 Initializing collections . . . . . . . . . . . . . .
17.5 Tuples . . . . . . . . . . . . . . . . . . . . . .
17.6 Conclusion . . . . . . . . . . . . . . . . . . . .
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373
373
377
385
388
392
394
18 Mutable Objects
18.1 What makes an object mutable? . . .
18.2 Reassignable variables and properties
18.3 Case study: Discrete event simulation
18.4 A language for digital circuits . . . .
18.5 The Simulation API . . . . . . . .
18.6 Circuit Simulation . . . . . . . . . .
18.7 Conclusion . . . . . . . . . . . . . .
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395
395
398
401
402
405
409
417
19 Type Parameterization
19.1 Functional queues . . . . . . .
19.2 Information hiding . . . . . . .
19.3 Variance annotations . . . . . .
19.4 Checking variance annotations .
19.5 Lower bounds . . . . . . . . .
19.6 Contravariance . . . . . . . . .
19.7 Object private data . . . . . . .
19.8 Upper bounds . . . . . . . . .
19.9 Conclusion . . . . . . . . . . .
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425
429
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20 Abstract Members
20.1 A quick tour of abstract members
20.2 Type members . . . . . . . . . .
20.3 Abstract vals . . . . . . . . . . .
20.4 Abstract vars . . . . . . . . . . .
20.5 Initializing abstract vals . . . . .
20.6 Abstract types . . . . . . . . . .
20.7 Path-dependent types . . . . . .
20.8 Refinement types . . . . . . . . .
20.9 Enumerations . . . . . . . . . . .
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Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
Contents
xvii
20.10 Case study: Currencies . . . . . . . . . . . . . . . . . .
20.11 Conclusion . . . . . . . . . . . . . . . . . . . . . . . .
21 Implicit Conversions and Parameters
21.1 Implicit conversions . . . . . . . . . .
21.2 Rules for implicits . . . . . . . . . . .
21.3 Implicit conversion to an expected type
21.4 Converting the receiver . . . . . . . .
21.5 Implicit parameters . . . . . . . . . . .
21.6 Context bounds . . . . . . . . . . . . .
21.7 When multiple conversions apply . . .
21.8 Debugging implicits . . . . . . . . . .
21.9 Conclusion . . . . . . . . . . . . . . .
Prepared for Library Genesis
22 Implementing Lists
22.1 The List class in principle
22.2 The ListBuffer class . . .
22.3 The List class in practice .
22.4 Functional on the outside .
22.5 Conclusion . . . . . . . . .
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463
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474
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494
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497
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499
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505
507
509
510
23 For Expressions Revisited
23.1 For expressions . . . . . . . . .
23.2 The n-queens problem . . . . .
23.3 Querying with for expressions
23.4 Translation of for expressions .
23.5 Going the other way . . . . . .
23.6 Generalizing for . . . . . . . .
23.7 Conclusion . . . . . . . . . . .
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528
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24 Collections in Depth
24.1 Mutable and immutable collections . . . . . . . . . .
24.2 Collections consistency . . . . . . . . . . . . . . . .
24.3 Trait Traversable . . . . . . . . . . . . . . . . . . .
24.4 Trait Iterable . . . . . . . . . . . . . . . . . . . . .
24.5 The sequence traits Seq, IndexedSeq, and LinearSeq
24.6 Sets . . . . . . . . . . . . . . . . . . . . . . . . . . .
24.7 Maps . . . . . . . . . . . . . . . . . . . . . . . . . .
Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
Contents
24.8
24.9
24.10
24.11
24.12
24.13
24.14
24.15
24.16
24.17
24.18
xviii
Concrete immutable collection classes . . . . .
Concrete mutable collection classes . . . . . . .
Arrays . . . . . . . . . . . . . . . . . . . . . .
Strings . . . . . . . . . . . . . . . . . . . . . .
Performance characteristics . . . . . . . . . . .
Equality . . . . . . . . . . . . . . . . . . . . .
Views . . . . . . . . . . . . . . . . . . . . . . .
Iterators . . . . . . . . . . . . . . . . . . . . .
Creating collections from scratch . . . . . . . .
Conversions between Java and Scala collections
Conclusion . . . . . . . . . . . . . . . . . . . .
25 The Architecture of Scala Collections
25.1 Builders . . . . . . . . . . . . .
25.2 Factoring out common operations
25.3 Integrating new collections . . .
25.4 Conclusion . . . . . . . . . . . .
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556
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597
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26 Extractors
26.1 An example: extracting email addresses
26.2 Extractors . . . . . . . . . . . . . . .
26.3 Patterns with zero or one variables . .
26.4 Variable argument extractors . . . . . .
26.5 Extractors and sequence patterns . . .
26.6 Extractors versus case classes . . . . .
26.7 Regular expressions . . . . . . . . . .
26.8 Conclusion . . . . . . . . . . . . . . .
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621
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28 Working with XML
28.1 Semi-structured data . . . . . . . . . . . . . . . . . . .
28.2 XML overview . . . . . . . . . . . . . . . . . . . . . .
28.3 XML literals . . . . . . . . . . . . . . . . . . . . . . .
645
645
646
647
27 Annotations
27.1 Why have annotations?
27.2 Syntax of annotations .
27.3 Standard annotations . .
27.4 Conclusion . . . . . . .
Prepared for Library Genesis
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Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
Contents
28.4
28.5
28.6
28.7
28.8
28.9
Serialization . . . . . . .
Taking XML apart . . . .
Deserialization . . . . . .
Loading and saving . . .
Pattern matching on XML
Conclusion . . . . . . . .
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xix
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29 Modular Programming Using Objects
29.1 The problem . . . . . . . . . . .
29.2 A recipe application . . . . . . .
29.3 Abstraction . . . . . . . . . . . .
29.4 Splitting modules into traits . . .
29.5 Runtime linking . . . . . . . . .
29.6 Tracking module instances . . . .
29.7 Conclusion . . . . . . . . . . . .
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Prepared for Library Genesis
30 Object Equality
30.1 Equality in Scala . . . . . . . . . . . . .
30.2 Writing an equality method . . . . . . .
30.3 Defining equality for parameterized types
30.4 Recipes for equals and hashCode . . .
30.5 Conclusion . . . . . . . . . . . . . . . .
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31 Combining Scala and Java
31.1 Using Scala from Java . . . . . . .
31.2 Annotations . . . . . . . . . . . .
31.3 Wildcard types . . . . . . . . . . .
31.4 Compiling Scala and Java together
31.5 Java 8 integration in Scala 2.12 . .
31.6 Conclusion . . . . . . . . . . . . .
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698
698
701
706
709
709
713
32 Futures and Concurrency
32.1 Trouble in paradise . . . . . . . .
32.2 Asynchronous execution and Trys
32.3 Working with Futures . . . . . .
32.4 Testing with Futures . . . . . .
32.5 Conclusion . . . . . . . . . . . .
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714
714
716
718
733
735
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Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
Contents
Prepared for Library Genesis
33 Combinator Parsing
33.1 Example: Arithmetic expressions . .
33.2 Running your parser . . . . . . . . .
33.3 Basic regular expression parsers . . .
33.4 Another example: JSON . . . . . . .
33.5 Parser output . . . . . . . . . . . . .
33.6 Implementing combinator parsers . .
33.7 String literals and regular expressions
33.8 Lexing and parsing . . . . . . . . . .
33.9 Error reporting . . . . . . . . . . . .
33.10 Backtracking versus LL(1) . . . . . .
33.11 Conclusion . . . . . . . . . . . . . .
xx
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736
737
739
740
741
743
749
758
759
759
761
763
34 GUI Programming
34.1 A first Swing application . . . . . . . .
34.2 Panels and layouts . . . . . . . . . . .
34.3 Handling events . . . . . . . . . . . .
34.4 Example: Celsius/Fahrenheit converter
34.5 Conclusion . . . . . . . . . . . . . . .
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765
765
768
770
773
775
35 The SCells Spreadsheet
35.1 The visual framework . . . . . . . .
35.2 Disconnecting data entry and display
35.3 Formulas . . . . . . . . . . . . . . .
35.4 Parsing formulas . . . . . . . . . . .
35.5 Evaluation . . . . . . . . . . . . . .
35.6 Operation libraries . . . . . . . . . .
35.7 Change propagation . . . . . . . . .
35.8 Conclusion . . . . . . . . . . . . . .
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777
777
780
783
785
790
793
796
800
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A Scala Scripts on Unix and Windows
802
Glossary
803
Bibliography
819
About the Authors
822
Index
823
Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
Prepared for Library Genesis
List of Figures
2.1
2.2
The basic form of a function definition in Scala. . . . . . . .
The syntax of a function literal in Scala. . . . . . . . . . . .
69
75
3.1
3.2
3.3
All operations are method calls in Scala. . . . . . . . . . . .
Class hierarchy for Scala sets. . . . . . . . . . . . . . . . .
Class hierarchy for Scala maps. . . . . . . . . . . . . . . . .
80
88
90
10.1
10.2
10.3
10.4
Class diagram for ArrayElement. . . . . .
Class diagram for LineElement. . . . . . .
Class hierarchy of layout elements. . . . . .
Class hierarchy with revised LineElement.
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224
229
232
236
11.1 Class hierarchy of Scala. . . . . . . . . . . . . . . . . . . .
248
12.1 Inheritance hierarchy and linearization of class Cat. . . . . .
273
14.1 The output of org.scalatest.run. . . . . . . . . . . . . .
305
18.1 Basic gates. . . . . . . . . . . . . . . . . . . . . . . . . . .
18.2 A half-adder circuit. . . . . . . . . . . . . . . . . . . . . . .
18.3 A full-adder circuit. . . . . . . . . . . . . . . . . . . . . . .
402
404
405
19.1 Covariance and contravariance in function type parameters. .
437
22.1 Class hierarchy for Scala lists. . . . . . . . . . . . . . . . .
22.2 The structure of the Scala lists shown in Listing 22.2. . . . .
500
504
24.1 Collection hierarchy. . . . . . . . . . . . . . . . . . . . . .
532
25.1 An example Patricia trie. . . . . . . . . . . . . . . . . . . .
615
xxi
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Prepared for Library Genesis
List of Figures
xxii
32.1 Class hierarchy for Try. . . . . . . . . . . . . . . . . . . . .
718
34.1 A simple Swing application: initial (left) and resized (right).
34.2 A reactive Swing application: initial (left) after clicks (right).
34.3 A converter between degrees Celsius and Fahrenheit. . . . .
766
768
773
35.1
35.2
35.3
35.4
778
783
789
795
A simple spreadsheet table. . .
Cells displaying themselves. .
Cells displaying their formulas.
Cells that evaluate. . . . . . .
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Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
Prepared for Library Genesis
List of Tables
3.1
Some List methods and usages . . . . . . . . . . . . . . .
5.1
5.2
5.3
5.4
5.5
Some basic types . . . . . . . . . . . . .
Special character literal escape sequences
Operator precedence . . . . . . . . . . .
Some rich operations . . . . . . . . . .
Rich wrapper classes . . . . . . . . . . .
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113
117
131
134
134
12.1
Linearization of types in Cat’s hierarchy . . . . . . . . . .
274
13.1
Effects of private qualifiers on LegOfJourney.distance .
289
16.1
Basic list operations . . . . . . . . . . . . . . . . . . . . .
343
17.1
17.2
17.3
17.4
Common operations for sets . . . . . . .
Common operations for maps . . . . . .
Default immutable set implementations .
Default immutable map implementations
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380
382
384
384
24.1
24.2
24.3
24.4
24.5
24.6
24.7
24.8
24.9
24.10
Operations in trait Traversable . . . . . . .
Operations in trait Iterable . . . . . . . . .
Operations in trait Seq . . . . . . . . . . . .
Operations in trait Buffer . . . . . . . . . .
Operations in trait Set . . . . . . . . . . . .
Operations in trait mutable.Set . . . . . . .
Operations in trait Map . . . . . . . . . . . .
Operations in trait mutable.Map . . . . . . .
Operations in trait ConcurrentMap . . . . . .
Performance characteristics of sequence types
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535
540
544
547
548
549
553
554
569
576
xxiii
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84
Prepared for Library Genesis
List of Tables
xxiv
24.11 Performance characteristics of set and map types . . . . . .
24.12 Operations in trait Iterator . . . . . . . . . . . . . . . .
24.13 Factory methods for sequences . . . . . . . . . . . . . . .
576
587
594
33.1
747
Summary of parser combinators . . . . . . . . . . . . . . .
Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
Prepared for Library Genesis
List of Listings
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
3.9
3.10
3.11
Parameterizing an array with a type. . . . . . . . . . . .
Creating and initializing an array. . . . . . . . . . . . . .
Creating and initializing a list. . . . . . . . . . . . . . . .
Creating and using a tuple. . . . . . . . . . . . . . . . .
Creating, initializing, and using an immutable set. . . . .
Creating, initializing, and using a mutable set. . . . . . .
Creating, initializing, and using a mutable map. . . . . .
Creating, initializing, and using an immutable map. . . .
A function without side effects or vars. . . . . . . . . . .
Reading lines from a file. . . . . . . . . . . . . . . . . .
Printing formatted character counts for the lines of a file.
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78
81
82
86
87
89
90
91
93
95
98
4.1
4.2
4.3
4.4
Final version of class ChecksumAccumulator. . . .
Companion object for class ChecksumAccumulator.
The Summer application. . . . . . . . . . . . . . . .
Using the App trait. . . . . . . . . . . . . . . . . .
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103
106
108
110
6.1
6.2
6.3
6.4
6.5
Rational with fields. . . . . . . . . . . .
Rational with an auxiliary constructor. .
Rational with a private field and method.
Rational with operator methods. . . . . .
Rational with overloaded methods. . . .
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141
143
144
146
151
7.1
7.2
7.3
7.4
7.5
Scala’s idiom for conditional initialization. . . . . . . .
Calculating greatest common divisor with a while loop.
Reading from the standard input with do-while. . . . .
Calculating greatest common divisor with recursion. . .
Listing files in a directory with a for expression. . . . .
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156
157
158
159
160
xxv
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Prepared for Library Genesis
List of Listings
xxvi
7.6
7.7
7.8
7.9
7.10
7.11
7.12
7.13
7.14
7.15
7.16
7.17
7.18
7.19
Finding .scala files using a for with a filter. . . . . . . .
Using multiple filters in a for expression. . . . . . . . . .
Using multiple generators in a for expression. . . . . . . .
Mid-stream assignment in a for expression. . . . . . . . .
Transforming an Array[File] to Array[Int] with a for.
A try-catch clause in Scala. . . . . . . . . . . . . . . . .
A try-finally clause in Scala. . . . . . . . . . . . . . .
A catch clause that yields a value. . . . . . . . . . . . . .
A match expression with side effects. . . . . . . . . . . . .
A match expression that yields a value. . . . . . . . . . . .
Looping without break or continue. . . . . . . . . . . . .
A recursive alternative to looping with vars. . . . . . . . .
Variable scoping when printing a multiplication table. . . .
A functional way to create a multiplication table. . . . . . .
162
162
163
164
165
167
168
169
170
170
172
172
175
178
8.1
8.2
8.3
8.4
LongLines with a private processLine method. . . . . . .
LongLines with a local processLine function. . . . . . .
A parameter with a default value. . . . . . . . . . . . . . .
A function with two parameters that have defaults. . . . . .
181
183
197
198
9.1
9.2
9.3
9.4
9.5
Using closures to reduce code duplication. .
Defining and invoking a “plain old” function.
Defining and invoking a curried function. . .
Using the loan pattern to write to a file. . . .
Using a by-name parameter. . . . . . . . . .
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207
210
210
214
215
10.1
10.2
10.3
10.4
10.5
10.6
10.7
10.8
10.9
10.10
10.11
10.12
Defining an abstract method and class. . . . . . . .
Defining parameterless methods width and height.
Defining ArrayElement as a subclass of Element. .
Overriding a parameterless method with a field. . .
Defining contents as a parametric field. . . . . . .
Invoking a superclass constructor. . . . . . . . . . .
Declaring a final method. . . . . . . . . . . . . . .
Declaring a final class. . . . . . . . . . . . . . . . .
Class Element with above, beside, and toString.
A factory object with factory methods. . . . . . . .
Class Element refactored to use factory methods. .
Hiding implementation with private classes. . . . .
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220
221
223
225
227
228
234
234
239
240
241
242
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Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
Prepared for Library Genesis
List of Listings
xxvii
10.13 Element with widen and heighten methods. . . . . . . .
10.14 The Spiral application. . . . . . . . . . . . . . . . . . . .
243
244
12.1
12.2
12.3
12.4
12.5
12.6
12.7
12.8
12.9
12.10
The definition of trait Philosophical. . . . . . . . . . . .
Mixing in a trait using extends. . . . . . . . . . . . . . .
Mixing in a trait using with. . . . . . . . . . . . . . . . .
Mixing in multiple traits. . . . . . . . . . . . . . . . . . .
Defining an enrichment trait. . . . . . . . . . . . . . . . .
Abstract class IntQueue. . . . . . . . . . . . . . . . . . .
A BasicIntQueue implemented with an ArrayBuffer. . .
The Doubling stackable modification trait. . . . . . . . . .
Mixing in a trait when instantiating with new. . . . . . . . .
Stackable modification traits Incrementing and Filtering.
257
258
259
259
263
267
267
268
269
269
13.1
13.2
13.3
13.4
13.5
13.6
13.7
13.8
13.9
13.10
13.11
13.12
13.13
13.14
Placing the contents of an entire file into a package. . . . .
Long form of a simple package declaration. . . . . . . . .
Multiple packages in the same file. . . . . . . . . . . . . .
Concise access to classes and packages. . . . . . . . . . . .
Symbols in enclosing packages not automatically available.
Accessing hidden package names. . . . . . . . . . . . . . .
Bob’s delightful fruits, ready for import. . . . . . . . . . .
Importing the members of a regular (not singleton) object. .
Importing a package name. . . . . . . . . . . . . . . . . .
How private access differs in Scala and Java. . . . . . . . .
How protected access differs in Scala and Java. . . . . . . .
Flexible scope of protection with access qualifiers. . . . . .
Accessing private members of companion classes and objects.
A package object. . . . . . . . . . . . . . . . . . . . . . .
277
277
278
278
279
279
282
282
283
286
287
288
291
292
14.1
14.2
14.3
14.4
14.5
14.6
14.7
Using an assertion. . . . . . . . . . . . . . . . . . . . . . .
Using ensuring to assert a function’s result. . . . . . . . .
Writing tests with FunSuite. . . . . . . . . . . . . . . . .
Specifying and testing behavior with a ScalaTest FlatSpec.
Specifying and testing behavior with the specs2 framework.
Using tests to facilitate communication among stakeholders.
Writing property-based tests with ScalaCheck. . . . . . . .
295
295
296
300
301
302
303
15.1
15.2
Defining case classes. . . . . . . . . . . . . . . . . . . . .
The simplifyTop function, which does a pattern match. . .
307
309
Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
Prepared for Library Genesis
List of Listings
xxviii
15.3
15.4
15.5
15.6
15.7
15.8
15.9
15.10
15.11
15.12
15.13
15.14
15.15
15.16
15.17
15.18
15.19
15.20
15.21
15.22
A pattern match with an empty “default” case. . . . . .
A pattern match with wildcard patterns. . . . . . . . . .
A pattern match with constant patterns. . . . . . . . . .
A pattern match with a variable pattern. . . . . . . . . .
A pattern match with a constructor pattern. . . . . . . .
A sequence pattern with a fixed length. . . . . . . . . .
A sequence pattern with an arbitrary length. . . . . . .
A pattern match with a tuple pattern. . . . . . . . . . .
A pattern match with typed patterns. . . . . . . . . . .
Using isInstanceOf and asInstanceOf (poor style). .
A pattern with a variable binding (via the @ sign). . . .
A match expression with a pattern guard. . . . . . . . .
Match expression in which case order matters. . . . . .
A sealed hierarchy of case classes. . . . . . . . . . . .
Defining multiple variables with one assignment. . . . .
A for expression with a tuple pattern. . . . . . . . . .
Picking elements of a list that match a pattern. . . . . .
The top half of the expression formatter. . . . . . . . .
The bottom half of the expression formatter. . . . . . .
An application that prints formatted expressions. . . . .
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311
312
312
313
315
315
316
316
317
318
320
321
322
324
327
331
331
334
335
338
16.1
A merge sort function for Lists. . . . . . . . . . . . . . .
356
17.1
Default map and set definitions in Predef. . . . . . . . . .
378
18.1
18.2
18.3
18.4
18.5
18.6
18.7
18.8
18.9
18.10
18.11
A mutable bank account class. . . . . . . . . . . . . . . . .
A class with public vars. . . . . . . . . . . . . . . . . . .
How public vars are expanded into getter and setter methods.
Defining getter and setter methods directly. . . . . . . . . .
Defining a getter and setter without an associated field. . .
The halfAdder method. . . . . . . . . . . . . . . . . . . .
The fullAdder method. . . . . . . . . . . . . . . . . . . .
The Simulation class. . . . . . . . . . . . . . . . . . . .
The first half of the BasicCircuitSimulation class. . . .
The second half of the BasicCircuitSimulation class. .
The CircuitSimulation class. . . . . . . . . . . . . . . .
396
398
399
399
400
403
404
406
410
411
415
19.1
19.2
A basic functional queue. . . . . . . . . . . . . . . . . . .
Hiding a primary constructor by making it private. . . . . .
421
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19.3
19.4
19.5
19.6
19.7
19.8
19.9
19.10
19.11
19.12
An apply factory method in a companion object. . .
Type abstraction for functional queues. . . . . . . .
A nonvariant (rigid) Cell class. . . . . . . . . . . .
A type parameter with a lower bound. . . . . . . . .
A contravariant output channel. . . . . . . . . . . .
Covariance and contravariance of Function1s. . . .
Demonstration of function type parameter variance.
An optimized functional queue. . . . . . . . . . . .
A Person class that mixes in the Ordered trait. . .
A merge sort function with an upper bound. . . . .
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423
424
427
433
434
435
436
438
440
440
20.1
20.2
20.3
20.4
20.5
20.6
20.7
20.8
20.9
20.10
20.11
20.12
20.13
20.14
Overriding abstract vals and parameterless methods. .
Declaring abstract vars. . . . . . . . . . . . . . . . . .
How abstract vars are expanded into getters and setters.
A trait that uses its abstract vals. . . . . . . . . . . . .
Pre-initialized fields in an anonymous class expression.
Pre-initialized fields in an object definition. . . . . . . .
Pre-initialized fields in a class definition. . . . . . . . .
Initializing a trait with lazy vals. . . . . . . . . . . . .
Modeling suitable food with an abstract type. . . . . . .
Implementing an abstract type in a subclass. . . . . . .
The US currency zone. . . . . . . . . . . . . . . . . . .
Currency zones for Europe and Japan. . . . . . . . . .
A converter object with an exchange rates map. . . . .
The full code of class CurrencyZone. . . . . . . . . .
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446
446
447
449
450
450
450
453
456
457
469
470
471
472
21.1
21.2
21.3
21.4
21.5
21.6
21.7
21.8
An implicit parameter list with multiple parameters. . . . .
A function with an upper bound. . . . . . . . . . . . . . .
A function with an implicit parameter. . . . . . . . . . . .
A function that uses an implicit parameter internally. . . . .
A function that uses implicitly. . . . . . . . . . . . . . .
A function with a context bound. . . . . . . . . . . . . . .
Sample code that uses an implicit parameter. . . . . . . . .
Sample code after type checking and insertion of implicits.
488
489
490
492
493
494
498
498
22.1
22.2
22.3
The definition of the Nil singleton object. . . . . . . . . .
Prepending a supertype element to a subtype list. . . . . . .
The definition of method :: (cons) in class List. . . . . .
501
503
503
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22.4
22.5
22.6
The definition of method ::: in class List. . . . . . . . .
The definition of method map in class List. . . . . . . . .
The definition of the :: subclass of List. . . . . . . . . .
505
507
508
25.1
25.2
25.3
25.4
25.5
25.6
25.7
25.8
25.9
25.10
25.11
An outline of the Builder class. . . . . . . . . . . .
Implementation of filter in TraversableLike. . .
Implementation of map in TraversableLike. . . . .
The CanBuildFrom trait. . . . . . . . . . . . . . . .
RNA Bases. . . . . . . . . . . . . . . . . . . . . . .
RNA strands class, first version. . . . . . . . . . . . .
RNA strands class, second version. . . . . . . . . . .
RNA strands class, final version. . . . . . . . . . . .
RNA companion object—final version. . . . . . . . .
An implementation of prefix maps with Patricia tries.
The companion object for prefix maps. . . . . . . . .
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598
600
602
602
605
606
608
612
613
616
619
26.1
26.2
26.3
26.4
26.5
26.6
26.7
The EMail string extractor object. . . . . . . . . .
The Twice string extractor object. . . . . . . . . .
The UpperCase string extractor object. . . . . . .
The Domain string extractor object. . . . . . . . .
The ExpandedEMail extractor object. . . . . . . .
An extractor that defines an unapplySeq method.
How the r method is defined in StringOps. . . .
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623
626
626
628
629
630
634
29.1
29.2
29.3
29.4
29.5
29.6
29.7
29.8
29.9
29.10
29.11
29.12
29.13
29.14
A simple Food entity class. . . . . . . . . . . . . . . . . .
Simple Recipe entity class. . . . . . . . . . . . . . . . . .
Food and Recipe examples for use in tests. . . . . . . . . .
Mock database and browser modules. . . . . . . . . . . . .
Database and browser modules with categories added. . . .
A Browser class with an abstract database val. . . . . . .
A Database class with abstract methods. . . . . . . . . . .
The SimpleDatabase object as a Database subclass. . . .
The SimpleBrowser object as a Browser subclass. . . . .
A student database and browser. . . . . . . . . . . . . . . .
A trait for food categories. . . . . . . . . . . . . . . . . . .
A Database class that mixes in the FoodCategories trait.
A SimpleDatabase object composed solely of mixins. . .
A SimpleFoods trait. . . . . . . . . . . . . . . . . . . . .
661
662
662
663
664
665
666
666
667
667
668
668
668
668
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29.15 A SimpleRecipes trait with a self type. . . . . . . . . . .
29.16 An app that dynamically selects a module implementation.
29.17 Using a singleton type. . . . . . . . . . . . . . . . . . . .
669
670
672
30.1
30.2
30.3
30.4
30.5
A superclass equals method that calls canEqual.
A subclass equals method that calls canEqual. .
Hierarchy for binary trees. . . . . . . . . . . . . .
A parameterized type with equals and hashCode.
Class Rational with equals and hashCode. . . .
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686
687
689
693
696
31.1
A Scala method that declares a Java throws clause. . . . .
703
32.1
Returning a future assertion to ScalaTest. . . . . . . . . . .
735
33.1
33.2
33.3
33.4
33.5
33.6
An arithmetic expression parser. . . . . . . . . . .
A regular expression parser for Java identifiers. . .
Data in JSON format. . . . . . . . . . . . . . . .
A simple JSON parser. . . . . . . . . . . . . . . .
A full JSON parser that returns meaningful results.
The ~ combinator method. . . . . . . . . . . . . .
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738
740
742
743
747
755
34.1
34.2
34.3
34.4
A simple Swing application in Scala. . . . . . .
Component assembly on a panel. . . . . . . . .
Implementing a reactive Swing application. . . .
An implementation of the temperature converter.
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766
768
772
774
35.1
35.2
35.3
35.4
35.5
35.6
35.7
35.8
35.9
Code for spreadsheet in Figure 35.1. . . . . . . . .
The main program for the spreadsheet application. .
A spreadsheet with a rendererComponent method.
First version of the Model class. . . . . . . . . . . .
Classes representing formulas. . . . . . . . . . . .
A spreadsheet that parses formulas. . . . . . . . . .
The evaluate method of trait Evaluator. . . . . .
A library for arithmetic operations. . . . . . . . . .
The finished spreadsheet component. . . . . . . . .
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779
780
781
782
784
788
791
793
799
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Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
Prepared for Library Genesis
Foreword
You’ve chosen a great time to pick up this book! Scala adoption keeps accelerating, our community is thriving, and job ads abound. Whether you’re programming for fun or profit (or both), Scala’s promise of joy and productivity
is proving hard to resist. To me, the true joy of programming comes from
tackling interesting challenges with simple, sophisticated solutions. Scala’s
mission is not just to make this possible, but enjoyable, and this book will
show you how.
I first experimented with Scala 2.5, and was immediately drawn to its
syntactic and conceptual regularity. When I ran into the irregularity that type
parameters couldn’t have type parameters themselves, I (timidly) walked up
to Martin Odersky at a conference in 2006 and proposed an internship to
remove that restriction. My contribution was accepted, bringing support for
type constructor polymorphism to Scala 2.7 and up. Since then, I’ve worked
on most other parts of the compiler. In 2012 I went from post-doc in Martin’s
lab to Scala team lead at Typesafe, as Scala, with version 2.10, graduated
from its pragmatic academic roots to a robust language for the enterprise.
Scala 2.10 was a turning point from fast-paced, feature-rich releases
based on academic research, towards a focus on simplification and increased
adoption in the enterprise. We shifted our attention to issues that won’t be
written up in dissertations, such as binary compatibility between major releases. To balance stability with our desire to keep evolving and refining
the Scala platform, we’re working towards a smaller core library, which we
aim to stabilize while evolving the platform as a whole. To enable this, my
first project as Scala tech lead was to begin modularizing the Scala standard
library in 2.11.
To reduce the rate of change, Typesafe also decided to alternate changing
the library and the compiler. This edition of Programming in Scala covers
Scala 2.12, which will be a compiler release sporting a new back-end and opCover · Overview · Contents · Discuss · Suggest · Glossary · Index
Foreword
xxxiii
timizer to make the most of Java 8’s new features. For interoperability with
Java and to enjoy the same benefits from JVM optimizations, Scala compiles functions to the same bytecode as the Java 8 compiler. Similarly, Scala
traits now compile to Java interfaces with default methods. Both compilation
schemes reduce the magic that older Scala compilers had to perform, aligning us more closely with the Java platform, while improving both compiletime and run-time performance, with a smoother binary compatibility story
to boot!
These improvement to the Java 8 platform are very exciting for Scala,
and it’s very rewarding to see Java align with the trend Scala has been setting for over a decade! There’s no doubt that Scala provides a much better
functional programming experience, with immutability by default, a uniform
treatment of expressions (there’s hardly a return statement in sight in this
book), pattern matching, definition-site variance (Java’s use-site variance
make function subtyping quite awkward), and so on! To be blunt, there’s
more to functional programming than nice syntax for lambdas.
As stewards of the language, our goal is to develop the core language as
much as to foster the ecosystem. Scala is successful because of the many
excellent libraries, outstanding IDEs and tools, and the friendly and ever
helpful members of our community. I’ve thoroughly enjoyed my first decade
of Scala—as an implementer of the language, it’s such a thrill and inspiration
to meet programmers having fun with Scala across so many domains.
I love programming in Scala, and I hope you will too. On behalf of the
Scala community, welcome!
Prepared for Library Genesis
Adriaan Moors
San Francisco, CA
January 14, 2016
Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
Prepared for Library Genesis
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,
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Prepared for Library Genesis
Acknowledgments
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’d also like to extend a special thanks to all of our readers who contributed comments. Your comments were very helpful to us in shaping this
into an even better book. We couldn’t print the names of everyone who contributed comments, but here are the names of readers who submitted at least
five comments during the eBook PrePrint™ stage by clicking on the Suggest
link, sorted first by the highest total number of comments submitted, then
alphabetically. Thanks goes to: David Biesack, Donn Stephan, Mats Henricson, Rob Dickens, Blair Zajac, Tony Sloane, Nigel Harrison, Javier Diaz
Soto, William Heelan, Justin Forder, Gregor Purdy, Colin Perkins, Bjarte
S. Karlsen, Ervin Varga, Eric Willigers, Mark Hayes, Martin Elwin, Calum
MacLean, Jonathan Wolter, Les Pruszynski, Seth Tisue, Andrei Formiga,
Dmitry Grigoriev, George Berger, Howard Lovatt, John P. Eurich, Marius
Scurtescu, Jeff Ervin, Jamie Webb, Kurt Zoglmann, Dean Wampler, Nikolaj
Lindberg, Peter McLain, Arkadiusz Stryjski, Shanky Surana, Craig Bordelon, Alexandre Patry, Filip Moens, Fred Janon, Jeff Heon, Boris Lorbeer,
Jim Menard, Tim Azzopardi, Thomas Jung, Walter Chang, Jeroen Dijkmeijer, Casey Bowman, Martin Smith, Richard Dallaway, Antony Stubbs, Lars
Westergren, Maarten Hazewinkel, Matt Russell, Remigiusz Michalowski,
Andrew Tolopko, Curtis Stanford, Joshua Cough, Zemian Deng, Christopher Rodrigues Macias, Juan Miguel Garcia Lopez, Michel Schinz, Peter
Moore, Randolph Kahle, Vladimir Kelman, Daniel Gronau, Dirk Detering,
Hiroaki Nakamura, Ole Hougaard, Bhaskar Maddala, David Bernard, Derek
Mahar, George Kollias, Kristian Nordal, Normen Mueller, Rafael Ferreira,
Binil Thomas, John Nilsson, Jorge Ortiz, Marcus Schulte, Vadim Gerassimov, Cameron Taggart, Jon-Anders Teigen, Silvestre Zabala, Will McQueen,
and Sam Owen.
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,
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xxxv
Acknowledgments
Prepared for Library Genesis
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 Escalate’s Stairway to Scala course,
which is in great part based on this book. Our many years of experience
teaching Stairway to Scala has 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.
Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
xxxvi
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 2.11.7,
except for those marked 2.12, which compile with 2.12.0-M3.
Prepared for Library Genesis
Who should read this book
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 objectoriented 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
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Introduction
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.
Prepared for Library Genesis
How to learn Scala
You will learn a lot about Scala simply by reading this book from cover to
cover. You can learn Scala faster and more thoroughly, though, if you do a
few extra things.
First of all, you can take advantage of the many program examples included in the book. Typing them in yourself is a way to force your mind
through each line of code. Trying variations is a way to make them more fun
and to make sure you really understand how they work.
Second, keep in touch with the numerous online forums. That way, you
and other Scala enthusiasts can help each other. There are numerous mailing
lists, discussion forums, a chat room, a wiki, and multiple Scala-specific
article feeds. Take some time to find ones that fit your information needs.
You will spend a lot less time stuck on little problems, so you can spend
your time on deeper, more important questions.
Finally, once you have read enough, take on a programming project of
your own. Work on a small program from scratch or develop an add-in to a
larger program. You can only go so far by reading.
Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
xxxviii
Introduction
EBook features
This book is available in both paper and PDF eBook form. The eBook is not
simply an electronic copy of the paper version of the book. While the content
is the same as in the paper version, the eBook has been carefully designed
and optimized for reading on a computer screen.
The first thing to notice is that most references within the eBook are
hyperlinked. If you select a reference to a chapter, figure, or glossary entry,
your PDF viewer should take you immediately to the selected item so that
you do not have to flip around to find it.
Additionally, at the bottom of each page in the eBook are a number of
navigation links. The Cover, Overview, and Contents links take you to the
front matter of the book. The Glossary and Index links take you to reference
parts of the book. Finally, the Discuss link takes you to an online forum
where you discuss questions with other readers, the authors, and the larger
Scala community. If you find a typo, or something you think could be explained better, please click on the Suggest link, which will take you to an
online web application where you can give the authors feedback.
Although the same pages appear in the eBook as in the printed book,
blank pages are removed and the remaining pages renumbered. The pages
are numbered differently so that it is easier for you to determine PDF page
numbers when printing only a portion of the eBook. The pages in the eBook
are, therefore, numbered exactly as your PDF viewer will number them.
Typographic conventions
The first time a term is used, it is italicized. Small code examples, such as
x + 1, are written inline with a mono-spaced font. Larger code examples are
put into mono-spaced quotation blocks like this:
def hello() = {
Prepared for Library Genesis
println("Hello, world!")
}
When interactive shells are shown, responses from the shell are shown in a
lighter font:
scala> 3 + 4
res0: Int = 7
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xxxix
Introduction
Content overview
• Chapter 1, “A Scalable Language,” gives an overview of Scala’s design
as well as the reasoning, and history, behind it.
• Chapter 2, “First Steps in Scala,” shows you how to do a number of basic programming tasks in Scala, without going into great detail about
how they work. The goal of this chapter is to get your fingers started
typing and running Scala code.
• Chapter 3, “Next Steps in Scala,” shows you several more basic programming tasks that will help you get up to speed quickly in Scala.
After completing this chapter, you should be able to start using Scala
for simple scripting tasks.
• Chapter 4, “Classes and Objects,” starts the in-depth coverage of Scala
with a description of its basic object-oriented building blocks and instructions on how to compile and run a Scala application.
• Chapter 5, “Basic Types and Operations,” covers Scala’s basic types,
their literals, the operations you can perform on them, how precedence
and associativity works, and what rich wrappers are.
• Chapter 6, “Functional Objects,” dives more deeply into the objectoriented features of Scala, using functional (i.e., immutable) rational
numbers as an example.
• Chapter 7, “Built-in Control Structures,” shows you how to use Scala’s
built-in control structures: if, while, for, try, and match.
• Chapter 8, “Functions and Closures,” provides in-depth coverage of
functions, the basic building block of functional languages.
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• Chapter 9, “Control Abstraction,” shows how to augment Scala’s basic
control structures by defining your own control abstractions.
• Chapter 10, “Composition and Inheritance,” discusses more of Scala’s
support for object-oriented programming. The topics are not as fundamental as those in Chapter 4, but they frequently arise in practice.
• Chapter 11, “Scala’s Hierarchy,” explains Scala’s inheritance hierarchy and discusses its universal methods and bottom types.
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Introduction
• Chapter 12, “Traits,” covers Scala’s mechanism for mixin composition. The chapter shows how traits work, describes common uses, and
explains how traits improve on traditional multiple inheritance.
• Chapter 13, “Packages and Imports,” discusses issues with programming in the large, including top-level packages, import statements, and
access control modifiers like protected and private.
• Chapter 14, “Assertions and Tests,” shows Scala’s assertion mechanism and gives a tour of several tools available for writing tests in
Scala, focusing on ScalaTest in particular.
• Chapter 15, “Case Classes and Pattern Matching,” introduces twin
constructs that support you when writing regular, non-encapsulated
data structures. Case classes and pattern matching are particularly
helpful for tree-like recursive data.
• Chapter 16, “Working with Lists,” explains in detail lists, which are
probably the most commonly used data structure in Scala programs.
• Chapter 17, “Working with Other Collections,” shows you how to use
the basic Scala collections, such as lists, arrays, tuples, sets, and maps.
• Chapter 18, “Mutable Objects,” explains mutable objects and the syntax Scala provides to express them. The chapter concludes with a case
study on discrete event simulation, which shows some mutable objects
in action.
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• Chapter 19, “Type Parameterization,” explains some of the techniques
for information hiding introduced in Chapter 13 by means of a concrete example: the design of a class for purely functional queues. The
chapter builds up to a description of variance of type parameters and
how it interacts with information hiding.
• Chapter 20, “Abstract Members,” describes all kinds of abstract members that Scala supports; not only methods, but also fields and types,
can be declared abstract.
• Chapter 21, “Implicit Conversions and Parameters,” covers two constructs that can help you omit tedious details from source code, letting
the compiler supply them instead.
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Introduction
• Chapter 22, “Implementing Lists,” describes the implementation of
class List. It is important to understand how lists work in Scala,
and furthermore the implementation demonstrates the use of several
of Scala’s features.
• Chapter 23, “For Expressions Revisited,” shows how for expressions
are translated to invocations of map, flatMap, filter, and foreach.
• Chapter 24, “Collections in Depth,” gives a detailed tour of the collections library.
• Chapter 25, “The Architecture of Scala Collections,” shows how the
collection library is built and how you can implement your own collections.
• Chapter 26, “Extractors,” shows how to pattern match against arbitrary
classes, not just case classes.
• Chapter 27, “Annotations,” shows how to work with language extension via annotation. The chapter describes several standard annotations and shows you how to make your own.
• Chapter 28, “Working with XML,” explains how to process XML in
Scala. The chapter shows you idioms for generating XML, parsing it,
and processing it once it is parsed.
• Chapter 29, “Modular Programming Using Objects,” shows how you
can use Scala’s objects as a modules system.
• Chapter 30, “Object Equality,” points out several issues to consider
when writing an equals method. There are several pitfalls to avoid.
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• Chapter 31, “Combining Scala and Java,” discusses issues that arise
when combining Scala and Java together in the same project, and suggests ways to deal with them.
• Chapter 32, “Futures and Concurrency,” shows you how to use Scala’s
Future. Although you can use the Java platform’s concurrency primitives and libraries for Scala programs, futures can help you avoid the
deadlocks and race conditions that plague the traditional “threads and
locks” approach to concurrency.
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Introduction
• Chapter 33, “Combinator Parsing,” shows how to build parsers using
Scala’s library of parser combinators.
• Chapter 34, “GUI Programming,” gives a quick tour of a Scala library
that simplifies GUI programming with Swing.
• Chapter 35, “The SCells Spreadsheet,” ties everything together by
showing a complete spreadsheet application written in Scala.
Resources
At http://www.scala-lang.org, the main website for Scala, you’ll find
the latest Scala release and links to documentation and community resources.
For a more condensed page of links to Scala resources, visit this book’s website: http://booksites.artima.com/programming_in_scala_3ed. To
interact with other readers of this book, check out the Programming in Scala
Forum, at: http://www.artima.com/forums/forum.jsp?forum=282.
Source code
You can download a ZIP file containing the source code of this book, which is
released under the Apache 2.0 open source license, from the book’s website:
http://booksites.artima.com/programming_in_scala_3ed.
Errata
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Although this book has been heavily reviewed and checked, errors will inevitably slip through. For a (hopefully short) list of errata for this book, visit
http://booksites.artima.com/programming_in_scala_3ed/errata.
If you find an error, please report it at the above URL, so that we can fix it in
a future printing or edition of this book.
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Programming in Scala
Third Edition
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println("Hello, reader!")
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Chapter 1
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A Scalable Language
The name Scala stands for “scalable language.” The language is so named
because it was designed to grow with the demands of its users. You can apply
Scala to a wide range of programming tasks, from writing small scripts to
building large systems.1
Scala is easy to get into. It runs on the standard Java platform and interoperates seamlessly with all Java libraries. It’s quite a good language
for writing scripts that pull together Java components. But it can apply its
strengths even more when used for building large systems and frameworks
of reusable components.
Technically, Scala is a blend of object-oriented and functional programming concepts in a statically typed language. The fusion of object-oriented
and functional programming shows up in many different aspects of Scala;
it is probably more pervasive than in any other widely used language. The
two programming styles have complementary strengths when it comes to
scalability. Scala’s functional programming constructs make it easy to build
interesting things quickly from simple parts. Its object-oriented constructs
make it easy to structure larger systems and adapt them to new demands.
The combination of both styles in Scala makes it possible to express new
kinds of programming patterns and component abstractions. It also leads to
a legible and concise programming style. And because it is so malleable,
programming in Scala can be a lot of fun.
This initial chapter answers the question, “Why Scala?” It gives a highlevel view of Scala’s design and the reasoning behind it. After reading the
chapter you should have a basic feel for what Scala is and what kinds of
1 Scala
is pronounced skah-lah.
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Section 1.1
Chapter 1 · A Scalable Language
tasks it might help you accomplish. Although this book is a Scala tutorial,
this chapter isn’t really part of the tutorial. If you’re eager to start writing
some Scala code, you should jump ahead to Chapter 2.
1.1
A language that grows on you
Programs of different sizes tend to require different programming constructs.
Consider, for example, the following small Scala program:
var capital = Map("US" -> "Washington", "France" -> "Paris")
capital += ("Japan" -> "Tokyo")
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println(capital("France"))
This program sets up a map from countries to their capitals, modifies the map
by adding a new binding ("Japan" -> "Tokyo"), and prints the capital associated with the country France.2 The notation in this example is high level, to
the point, and not cluttered with extraneous semicolons or type annotations.
Indeed, the feel is that of a modern “scripting” language like Perl, Python, or
Ruby. One common characteristic of these languages, which is relevant for
the example above, is that they each support an “associative map” construct
in the syntax of the language.
Associative maps are very useful because they help keep programs legible and concise, but sometimes you might not agree with their “one size
fits all” philosophy because you need to control the properties of the maps
you use in your program in a more fine-grained way. Scala gives you this
fine-grained control if you need it, because maps in Scala are not language
syntax. They are library abstractions that you can extend and adapt.
In the above program, you’ll get a default Map implementation, but you
can easily change that. You could for example specify a particular implementation, such as a HashMap or a TreeMap, or invoke the par method to
obtain a ParMap that executes operations in parallel. You could specify a default value for the map, or you could override any other method of the map
you create. In each case, you can use the same easy access syntax for maps
as in the example above.
2 Please
bear with us if you don’t understand all the details of this program. They will be
explained in the next two chapters.
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Section 1.1
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This example shows that Scala can give you both convenience and flexibility. Scala has a set of convenient constructs that help you get started
quickly and let you program in a pleasantly concise style. At the same time,
you have the assurance that you will not outgrow the language. You can always tailor the program to your requirements, because everything is based
on library modules that you can select and adapt as needed.
Growing new types
Eric Raymond introduced the cathedral and bazaar as two metaphors of software development.3 The cathedral is a near-perfect building that takes a long
time to build. Once built, it stays unchanged for a long time. The bazaar, by
contrast, is adapted and extended each day by the people working in it. In
Raymond’s work the bazaar is a metaphor for open-source software development. Guy Steele noted in a talk on “growing a language” that the same
distinction can be applied to language design.4 Scala is much more like a
bazaar than a cathedral, in the sense that it is designed to be extended and
adapted by the people programming in it. Instead of providing all constructs
you might ever need in one “perfectly complete” language, Scala puts the
tools for building such constructs into your hands.
Here’s an example. Many applications need a type of integer that can
become arbitrarily large without overflow or “wrap-around” of arithmetic
operations. Scala defines such a type in library class scala.BigInt. Here
is the definition of a method using that type, which calculates the factorial of
a passed integer value:5
def factorial(x: BigInt): BigInt =
if (x == 0) 1 else x * factorial(x - 1)
Now, if you call factorial(30) you would get:
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265252859812191058636308480000000
BigInt looks like a built-in type because you can use integer literals and
operators such as * and - with values of that type. Yet it is just a class that
3 Raymond,
The Cathedral and the Bazaar. [Ray99]
“Growing a language.” [Ste99]
5 factorial(x), or x! in mathematical notation, is the result of computing
1 * 2 * ... * x, with 0! defined to be 1.
4 Steele,
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Section 1.1
Chapter 1 · A Scalable Language
happens to be defined in Scala’s standard library.6 If the class were missing,
it would be straightforward for any Scala programmer to write an implementation, for instance, by wrapping Java’s class java.math.BigInteger (in
fact that’s how Scala’s BigInt class is implemented).
Of course, you could also use Java’s class directly. But the result is not
nearly as pleasant, because although Java allows you to create new types,
those types don’t feel much like native language support:
import java.math.BigInteger
def factorial(x: BigInteger): BigInteger =
if (x == BigInteger.ZERO)
BigInteger.ONE
else
x.multiply(factorial(x.subtract(BigInteger.ONE)))
BigInt is representative of many other number-like types—big decimals,
complex numbers, rational numbers, confidence intervals, polynomials—the
list goes on. Some programming languages implement some of these types
natively. For instance, Lisp, Haskell, and Python implement big integers;
Fortran and Python implement complex numbers. But any language that
attempted to implement all of these abstractions at the same time would simply become too big to be manageable. What’s more, even if such a language
were to exist, some applications would surely benefit from other numberlike types that were not supplied. So the approach of attempting to provide
everything in one language doesn’t scale very well. Instead, Scala allows
users to grow and adapt the language in the directions they need by defining
easy-to-use libraries that feel like native language support.
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Growing new control constructs
The previous example demonstrates that Scala lets you add new types that
can be used as conveniently as built-in types. The same extension principle
also applies to control structures. This kind of extensibility is illustrated by
Akka, a Scala API for “actor-based” concurrent programming.
6 Scala
comes with a standard library, some of which will be covered in this book. For
more information, you can also consult the library’s Scaladoc documentation, which is available in the distribution and online at http://www.scala-lang.org.
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Section 1.1
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As multicore processors continue to proliferate in the coming years,
achieving acceptable performance may increasingly require that you exploit
more parallelism in your applications. Often, this will mean rewriting your
code so that computations are distributed over several concurrent threads.
Unfortunately, creating dependable multi-threaded applications has proven
challenging in practice. Java’s threading model is built around shared memory and locking, a model that is often difficult to reason about, especially as
systems scale up in size and complexity. It is hard to be sure you don’t have
a race condition or deadlock lurking—something that didn’t show up during
testing, but might just show up in production. An arguably safer alternative
is a message passing architecture, such as the “actors” approach used by the
Erlang programming language.
Java comes with a rich, thread-based concurrency library. Scala programs can use it like any other Java API. However, Akka is an additional
Scala library that implements an actor model similar to Erlang’s.
Actors are concurrency abstractions that can be implemented on top of
threads. They communicate by sending messages to each other. An actor can
perform two basic operations, message send and receive. The send operation,
denoted by an exclamation point (!), sends a message to an actor. Here’s an
example in which the actor is named recipient:
recipient ! msg
A send is asynchronous; that is, the sending actor can proceed immediately,
without waiting for the message to be received and processed. Every actor
has a mailbox in which incoming messages are queued. An actor handles
messages that have arrived in its mailbox via a receive block:
def receive = {
case Msg1 => ... // handle Msg1
case Msg2 => ... // handle Msg2
// ...
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}
A receive block consists of a number of cases that each query the mailbox
with a message pattern. The first message in the mailbox that matches any of
the cases is selected, and the corresponding action is performed on it. Once
the mailbox does not contain any messages, the actor suspends and waits for
further incoming messages.
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Section 1.1
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As an example, here is a simple Akka actor implementing a checksum
calculator service:
class ChecksumActor extends Actor {
var sum = 0
def receive = {
case Data(byte) => sum += byte
case GetChecksum(requester) =>
val checksum = ~(sum & 0xFF) + 1
requester ! checksum
}
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}
This actor first defines a local variable named sum with initial value zero.
It defines a receive block that will handle messages. If it receives a Data
message, it adds the contained byte to the sum variable. If it receives a
GetChecksum message, it calculates a checksum from the current value of
sum and sends the result back to the requester using the message send
requester ! sum. The requester field is embedded in the GetChecksum
message; it usually refers to the actor that made the request.
We don’t expect you to fully understand the actor example at this point.
Rather, what’s significant about this example for the topic of scalability is
that neither the receive block nor message send (!) are built-in operations
in Scala. Even though the receive block may look and act very much like
a built-in control construct, it is in fact a method defined in Akka’s actors
library. Likewise, even though ‘!’ looks like a built-in operator, it too is just
a method defined in the Akka actors library. Both of these constructs are
completely independent of the Scala programming language.
The receive block and send (!) syntax look in Scala much like they
look in Erlang, but in Erlang, these constructs are built into the language.
Akka also implements most of Erlang’s other concurrent programming constructs, such as monitoring failed actors and time-outs. All in all, the actor
model has turned out to be a very pleasant means for expressing concurrent
and distributed computations. Even though they must be defined in a library,
actors can feel like an integral part of the Scala language.
This example illustrates that you can “grow” the Scala language in new
directions even as specialized as concurrent programming. To be sure, you
need good architects and programmers to do this. But the crucial thing is
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Section 1.2
Chapter 1 · A Scalable Language
that it is feasible—you can design and implement abstractions in Scala that
address radically new application domains, yet still feel like native language
support when used.
1.2
What makes Scala scalable?
Scalability is influenced by many factors, ranging from syntax details to
component abstraction constructs. If we were forced to name just one aspect of Scala that helps scalability, though, we’d pick its combination of
object-oriented and functional programming (well, we cheated, that’s really
two aspects, but they are intertwined).
Scala goes further than all other well-known languages in fusing objectoriented and functional programming into a uniform language design. For
instance, where other languages might have objects and functions as two different concepts, in Scala a function value is an object. Function types are
classes that can be inherited by subclasses. This might seem nothing more
than an academic nicety, but it has deep consequences for scalability. In fact
the actor concept shown previously could not have been implemented without this unification of functions and objects. This section gives an overview
of Scala’s way of blending object-oriented and functional concepts.
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Scala is object-oriented
Object-oriented programming has been immensely successful. Starting from
Simula in the mid-60s and Smalltalk in the 70s, it is now available in more
languages than not. In some domains, objects have taken over completely.
While there is not a precise definition of what object-oriented means, there
is clearly something about objects that appeals to programmers.
In principle, the motivation for object-oriented programming is very simple: all but the most trivial programs need some sort of structure. The most
straightforward way to do this is to put data and operations into some form of
containers. The great idea of object-oriented programming is to make these
containers fully general, so that they can contain operations as well as data,
and that they are themselves values that can be stored in other containers, or
passed as parameters to operations. Such containers are called objects. Alan
Kay, the inventor of Smalltalk, remarked that in this way the simplest object
has the same construction principle as a full computer: it combines data with
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Section 1.2
Chapter 1 · A Scalable Language
operations under a formalized interface.7 So objects have a lot to do with
language scalability: the same techniques apply to the construction of small
as well as large programs.
Even though object-oriented programming has been mainstream for a
long time, there are relatively few languages that have followed Smalltalk
in pushing this construction principle to its logical conclusion. For instance,
many languages admit values that are not objects, such as the primitive values in Java. Or they allow static fields and methods that are not members
of any object. These deviations from the pure idea of object-oriented programming look quite harmless at first, but they have an annoying tendency
to complicate things and limit scalability.
By contrast, Scala is an object-oriented language in pure form: every
value is an object and every operation is a method call. For example, when
you say 1 + 2 in Scala, you are actually invoking a method named + defined
in class Int. You can define methods with operator-like names that clients
of your API can then use in operator notation. This is how the designer of
Akka’s actors API enabled you to use expressions such as requester ! sum
shown in the previous example: ‘!’ is a method of the Actor class.
Scala is more advanced than most other languages when it comes to composing objects. An example is Scala’s traits. Traits are like interfaces in Java,
but they can also have method implementations and even fields.8 Objects are
constructed by mixin composition, which takes the members of a class and
adds the members of a number of traits to them. In this way, different aspects of classes can be encapsulated in different traits. This looks a bit like
multiple inheritance, but differs when it comes to the details. Unlike a class,
a trait can add some new functionality to an unspecified superclass. This
makes traits more “pluggable” than classes. In particular, it avoids the classical “diamond inheritance” problems of multiple inheritance, which arise
when the same class is inherited via several different paths.
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Scala is functional
In addition to being a pure object-oriented language, Scala is also a fullblown functional language. The ideas of functional programming are older
than (electronic) computers. Their foundation was laid in Alonzo Church’s
7 Kay,
“The Early History of Smalltalk.” [Kay96]
with Java 8, interfaces can have default method implementations, but these do
not offer all the features of Scala’s traits.
8 Starting
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Section 1.2
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lambda calculus, which he developed in the 1930s. The first functional programming language was Lisp, which dates from the late 50s. Other popular
functional languages are Scheme, SML, Erlang, Haskell, OCaml, and F#.
For a long time, functional programming has been a bit on the sidelines—
popular in academia, but not that widely used in industry. However, in recent years, there has been an increased interest in functional programming
languages and techniques.
Functional programming is guided by two main ideas. The first idea is
that functions are first-class values. In a functional language, a function is a
value of the same status as, say, an integer or a string. You can pass functions as arguments to other functions, return them as results from functions,
or store them in variables. You can also define a function inside another
function, just as you can define an integer value inside a function. And you
can define functions without giving them a name, sprinkling your code with
function literals as easily as you might write integer literals like 42.
Functions that are first-class values provide a convenient means for abstracting over operations and creating new control structures. This generalization of functions provides great expressiveness, which often leads to very
legible and concise programs. It also plays an important role for scalability.
As an example, the ScalaTest testing library offers an eventually construct
that takes a function as an argument. It is used like this:
val xs = 1 to 3
val it = xs.iterator
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eventually { it.next() shouldBe 3 }
The code inside eventually—the assertion, it.next() shouldBe 3—is
wrapped in a function that is passed unexecuted to the eventually method.
For a configured amount of time, eventually will repeatedly execute the
function until the assertion succeeds.
In most traditional languages, by contrast, functions are not values. Languages that do have function values often relegate them to second-class status. For example, the function pointers of C and C++ do not have the same
status as non-functional values in those languages: Function pointers can
only refer to global functions, they do not allow you to define first-class
nested functions that refer to some values in their environment. Nor do they
allow you to define unnamed function literals.
The second main idea of functional programming is that the operations
of a program should map input values to output values rather than change
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data in place. To see the difference, consider the implementation of strings
in Ruby and Java. In Ruby, a string is an array of characters. Characters in a
string can be changed individually. For instance you can change a semicolon
character in a string to a period inside the same string object. In Java and
Scala, on the other hand, a string is a sequence of characters in the mathematical sense. Replacing a character in a string using an expression like
s.replace(';', '.') yields a new string object, which is different from s.
Another way of expressing this is that strings are immutable in Java whereas
they are mutable in Ruby. So looking at just strings, Java is a functional
language, whereas Ruby is not. Immutable data structures are one of the
cornerstones of functional programming. The Scala libraries define many
more immutable data types on top of those found in the Java APIs. For instance, Scala has immutable lists, tuples, maps, and sets.
Another way of stating this second idea of functional programming is
that methods should not have any side effects. They should communicate
with their environment only by taking arguments and returning results. For
instance, the replace method in Java’s String class fits this description. It
takes a string and two characters and yields a new string where all occurrences of one character are replaced by the other. There is no other effect of
calling replace. Methods like replace are called referentially transparent,
which means that for any given input the method call could be replaced by
its result without affecting the program’s semantics.
Functional languages encourage immutable data structures and referentially transparent methods. Some functional languages even require them.
Scala gives you a choice. When you want to, you can write in an imperative style, which is what programming with mutable data and side effects
is called. But Scala generally makes it easy to avoid imperative constructs
when you want because good functional alternatives exist.
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1.3
Why Scala?
Is Scala for you? You will have to see and decide for yourself. We have found
that there are actually many reasons besides scalability to like programming
in Scala. Four of the most important aspects will be discussed in this section:
compatibility, brevity, high-level abstractions, and advanced static typing.
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Scala is compatible
Scala doesn’t require you to leap backwards off the Java platform to step forward from the Java language. It allows you to add value to existing code—to
build on what you already have—because it was designed for seamless interoperability with Java.9 Scala programs compile to JVM bytecodes. Their
run-time performance is usually on par with Java programs. Scala code can
call Java methods, access Java fields, inherit from Java classes, and implement Java interfaces. None of this requires special syntax, explicit interface
descriptions, or glue code. In fact, almost all Scala code makes heavy use of
Java libraries, often without programmers being aware of this fact.
Another aspect of full interoperability is that Scala heavily re-uses Java
types. Scala’s Ints are represented as Java primitive integers of type int,
Floats are represented as floats, Booleans as booleans, and so on. Scala
arrays are mapped to Java arrays. Scala also re-uses many of the standard Java library types. For instance, the type of a string literal "abc" in
Scala is java.lang.String, and a thrown exception must be a subclass of
java.lang.Throwable.
Scala not only re-uses Java’s types, but also “dresses them up” to make
them nicer. For instance, Scala’s strings support methods like toInt or
toFloat, which convert the string to an integer or floating-point number.
So you can write str.toInt instead of Integer.parseInt(str). How
can this be achieved without breaking interoperability? Java’s String class
certainly has no toInt method! In fact, Scala has a very general solution
to solve this tension between advanced library design and interoperability.
Scala lets you define implicit conversions, which are always applied when
types would not normally match up, or when non-existing members are selected. In the case above, when looking for a toInt method on a string, the
Scala compiler will find no such member of class String, but it will find an
implicit conversion that converts a Java String to an instance of the Scala
class StringOps, which does define such a member. The conversion will
then be applied implicitly before performing the toInt operation.
Scala code can also be invoked from Java code. This is sometimes a bit
more subtle, because Scala is a richer language than Java, so some of Scala’s
9 Originally, there was an implementation of Scala that ran on the .NET platform, but it is
no longer active. More recently, an implementation of Scala that runs on JavaScript, Scala.js,
has become increasingly popular.
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Section 1.3
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more advanced features need to be encoded before they can be mapped to
Java. Chapter 31 explains the details.
Scala is concise
Scala programs tend to be short. Scala programmers have reported reductions in number of lines of up to a factor of ten compared to Java. These
might be extreme cases. A more conservative estimate would be that a typical Scala program should have about half the number of lines of the same
program written in Java. Fewer lines of code mean not only less typing, but
also less effort at reading and understanding programs and fewer possibilities of defects. There are several factors that contribute to this reduction in
lines of code.
First, Scala’s syntax avoids some of the boilerplate that burdens Java
programs. For instance, semicolons are optional in Scala and are usually left
out. There are also several other areas where Scala’s syntax is less noisy.
As an example, compare how you write classes and constructors in Java and
Scala. In Java, a class with a constructor often looks like this:
// this is Java
class MyClass {
private int index;
private String name;
public MyClass(int index, String name) {
this.index = index;
this.name = name;
}
}
In Scala, you would likely write this instead:
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class MyClass(index: Int, name: String)
Given this code, the Scala compiler will produce a class that has two private
instance variables, an Int named index and a String named name, and a
constructor that takes initial values for those variables as parameters. The
code of this constructor will initialize the two instance variables with the
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Section 1.3
Chapter 1 · A Scalable Language
values passed as parameters. In short, you get essentially the same functionality as the more verbose Java version.10 The Scala class is quicker to write,
easier to read, and most importantly, less error prone than the Java class.
Scala’s type inference is another factor that contributes to its conciseness. Repetitive type information can be left out, so programs become less
cluttered and more readable.
But probably the most important key to compact code is code you don’t
have to write because it is done in a library for you. Scala gives you many
tools to define powerful libraries that let you capture and factor out common
behavior. For instance, different aspects of library classes can be separated
out into traits, which can then be mixed together in flexible ways. Or, library methods can be parameterized with operations, which lets you define
constructs that are, in effect, your own control structures. Together, these
constructs allow the definition of libraries that are both high-level and flexible to use.
Scala is high-level
Programmers are constantly grappling with complexity. To program productively, you must understand the code on which you are working. Overly
complex code has been the downfall of many a software project. Unfortunately, important software usually has complex requirements. Such complexity can’t be avoided; it must instead be managed.
Scala helps you manage complexity by letting you raise the level of abstraction in the interfaces you design and use. As an example, imagine you
have a String variable name, and you want to find out whether or not that
String contains an upper case character. Prior to Java 8, you might have
written a loop, like this:
boolean nameHasUpperCase = false;
// this is Java
for (int i = 0; i < name.length(); ++i) {
if (Character.isUpperCase(name.charAt(i))) {
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nameHasUpperCase = true;
break;
}
}
10 The only real difference is that the instance variables produced in the Scala case will be
final. You’ll learn how to make them non-final in Section 10.6.
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Section 1.3
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Whereas in Scala, you could write this:
val nameHasUpperCase = name.exists(_.isUpper)
The Java code treats strings as low-level entities that are stepped through
character by character in a loop. The Scala code treats the same strings
as higher-level sequences of characters that can be queried with predicates.
Clearly the Scala code is much shorter and—for trained eyes—easier to understand than the Java code. So the Scala code weighs less heavily on the
total complexity budget. It also gives you less opportunity to make mistakes.
The predicate _.isUpper is an example of a function literal in Scala.11
It describes a function that takes a character argument (represented by the
underscore character) and tests whether it is an upper case letter.12
Java 8 introduced support for lambdas and streams, which enable you to
perform a similar operation in Java. Here’s what it might look like:
boolean nameHasUpperCase =
// This is Java 8
name.chars().anyMatch(
(int ch) -> Character.isUpperCase((char) ch)
);
Although a great improvement over earlier versions of Java, the Java 8
code is still more verbose than the equivalent Scala code. This extra “heaviness” of Java code, as well as Java’s long tradition of loops, may encourage
many Java programmers in need of new methods like exists to just write
out loops and live with the increased complexity in their code.
On the other hand, function literals in Scala are really lightweight, so
they are used frequently. As you get to know Scala better you’ll find more
and more opportunities to define and use your own control abstractions.
You’ll find that this helps avoid code duplication and thus keeps your programs shorter and clearer.
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Scala is statically typed
A static type system classifies variables and expressions according to the
kinds of values they hold and compute. Scala stands out as a language with
a very advanced static type system. Starting from a system of nested class
11 A
function literal can be called a predicate if its result type is Boolean.
use of the underscore as a placeholder for arguments is described in Section 8.5.
12 This
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Section 1.3
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types much like Java’s, it allows you to parameterize types with generics, to
combine types using intersections, and to hide details of types using abstract
types.13 These give a strong foundation for building and composing your
own types, so that you can design interfaces that are at the same time safe
and flexible to use.
If you like dynamic languages, such as Perl, Python, Ruby, or Groovy,
you might find it a bit strange that Scala’s static type system is listed as one
of its strong points. After all, the absence of a static type system has been
cited by some as a major advantage of dynamic languages. The most common arguments against static types are that they make programs too verbose,
prevent programmers from expressing themselves as they wish, and make
impossible certain patterns of dynamic modifications of software systems.
However, often these arguments do not go against the idea of static types in
general, but against specific type systems, which are perceived to be too verbose or too inflexible. For instance, Alan Kay, the inventor of the Smalltalk
language, once remarked: “I’m not against types, but I don’t know of any
type systems that aren’t a complete pain, so I still like dynamic typing.”14
We hope to convince you in this book that Scala’s type system is far from
being a “complete pain.” In fact, it addresses nicely two of the usual concerns about static typing: Verbosity is avoided through type inference, and
flexibility is gained through pattern matching and several new ways to write
and compose types. With these impediments out of the way, the classical
benefits of static type systems can be better appreciated. Among the most
important of these benefits are verifiable properties of program abstractions,
safe refactorings, and better documentation.
Verifiable properties. Static type systems can prove the absence of certain
run-time errors. For instance, they can prove properties like: Booleans are
never added to integers; private variables are not accessed from outside their
class; functions are applied to the right number of arguments; only strings
are ever added to a set of strings.
Other kinds of errors are not detected by today’s static type systems.
For instance, they will usually not detect non-terminating functions, array
bounds violations, or divisions by zero. They will also not detect that your
13 Generics are discussed in Chapter 19; intersections (e.g., A with B with C) in Chapter 12; and abstract types in Chapter 20.
14 Kay, in an email on the meaning of object-oriented programming. [Kay03]
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Section 1.3
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program does not conform to its specification (assuming there is a spec, that
is!). Static type systems have therefore been dismissed by some as not being
very useful. The argument goes that since such type systems can only detect simple errors, whereas unit tests provide more extensive coverage, why
bother with static types at all? We believe that these arguments miss the
point. Although a static type system certainly cannot replace unit testing, it
can reduce the number of unit tests needed by taking care of some properties
that would otherwise need to be tested. Likewise, unit testing cannot replace
static typing. After all, as Edsger Dijkstra said, testing can only prove the
presence of errors, never their absence.15 So the guarantees that static typing
gives may be simple, but they are real guarantees of a form no amount of
testing can deliver.
Safe refactorings. A static type system provides a safety net that lets you
make changes to a codebase with a high degree of confidence. Consider for
instance a refactoring that adds an additional parameter to a method. In a
statically typed language you can do the change, re-compile your system,
and simply fix all lines that cause a type error. Once you have finished with
this, you are sure to have found all places that need to be changed. The same
holds for many other simple refactorings, like changing a method name or
moving methods from one class to another. In all cases a static type check
will provide enough assurance that the new system works just like the old.
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Documentation. Static types are program documentation that is checked
by the compiler for correctness. Unlike a normal comment, a type annotation can never be out of date (at least not if the source file that contains it
has recently passed a compiler). Furthermore, compilers and integrated development environments (IDEs) can make use of type annotations to provide
better context help. For instance, an IDE can display all the members available for a selection by determining the static type of the expression on which
the selection is made and looking up all members of that type.
Even though static types are generally useful for program documentation,
they can sometimes be annoying when they clutter the program. Typically,
useful documentation is what readers of a program cannot easily derive by
themselves. In a method definition like:
15 Dijkstra,
“Notes on Structured Programming.” [Dij70]
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Section 1.4
Chapter 1 · A Scalable Language
def f(x: String) = ...
it’s useful to know that f’s argument should be a String. On the other hand,
at least one of the two annotations in the following example is annoying:
val x: HashMap[Int, String] = new HashMap[Int, String]()
Clearly, it should be enough to say just once that x is a HashMap with Ints as
keys and Strings as values; there’s no need to repeat the same phrase twice.
Scala has a very sophisticated type inference system that lets you omit
almost all type information that’s usually considered annoying. In the previous example, the following two less annoying alternatives would work just
as well:
val x = new HashMap[Int, String]()
val x: Map[Int, String] = new HashMap()
Type inference in Scala can go quite far. In fact, it’s not uncommon for
user code to have no explicit types at all. Therefore, Scala programs often
look a bit like programs written in a dynamically typed scripting language.
This holds particularly for client application code, which glues together prewritten library components. It’s less true for the library components themselves, because these often employ fairly sophisticated types to allow flexible
usage patterns. This is only natural. After all, the type signatures of the members that make up the interface of a reusable component should be explicitly
given, because they constitute an essential part of the contract between the
component and its clients.
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1.4
Scala’s roots
Scala’s design has been influenced by many programming languages and
ideas in programming language research. In fact, only a few features of
Scala are genuinely new; most have been already applied in some form in
other languages. Scala’s innovations come primarily from how its constructs
are put together. In this section, we list the main influences on Scala’s design.
The list cannot be exhaustive—there are simply too many smart ideas around
in programming language design to enumerate them all here.
At the surface level, Scala adopts a large part of the syntax of Java and
C#, which in turn borrowed most of their syntactic conventions from C and
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Section 1.4
Chapter 1 · A Scalable Language
C++. Expressions, statements, and blocks are mostly as in Java, as is the
syntax of classes, packages and imports.16 Besides syntax, Scala adopts
other elements of Java, such as its basic types, its class libraries, and its
execution model.
Scala also owes much to other languages. Its uniform object model was
pioneered by Smalltalk and taken up subsequently by Ruby. Its idea of universal nesting (almost every construct in Scala can be nested inside any other
construct) is also present in Algol, Simula, and, more recently, in Beta and
gbeta. Its uniform access principle for method invocation and field selection comes from Eiffel. Its approach to functional programming is quite
similar in spirit to the ML family of languages, which has SML, OCaml,
and F# as prominent members. Many higher-order functions in Scala’s standard library are also present in ML or Haskell. Scala’s implicit parameters
were motivated by Haskell’s type classes; they achieve analogous results in a
more classical object-oriented setting. Scala’s main actor-based concurrency
library, Akka, was heavily inspired by Erlang.
Scala is not the first language to emphasize scalability and extensibility. The historic root of extensible languages that can span different application areas is Peter Landin’s 1966 paper, “The Next 700 Programming Languages.”17 (The language described in this paper, Iswim, stands beside Lisp
as one of the pioneering functional languages.) The specific idea of treating
an infix operator as a function can be traced back to Iswim and Smalltalk.
Another important idea is to permit a function literal (or block) as a parameter, which enables libraries to define control structures. Again, this goes
back to Iswim and Smalltalk. Smalltalk and Lisp both have a flexible syntax
that has been applied extensively for building internal domain-specific languages. C++ is another scalable language that can be adapted and extended
through operator overloading and its template system; compared to Scala it
is built on a lower-level, more systems-oriented core.
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16
The major deviation from Java concerns the syntax for type annotations: it’s
“variable: Type” instead of “Type variable” in Java. Scala’s postfix type syntax resembles Pascal, Modula-2, or Eiffel. The main reason for this deviation has to do with type
inference, which often lets you omit the type of a variable or the return type of a method.
Using the “variable: Type” syntax is easy—just leave out the colon and the type. But in
C-style “Type variable” syntax you cannot simply leave off the type; there would be no
marker to start the definition anymore. You’d need some alternative keyword to be a placeholder for a missing type (C# 3.0, which does some type inference, uses var for this purpose).
Such an alternative keyword feels more ad-hoc and less regular than Scala’s approach.
17 Landin, “The Next 700 Programming Languages.” [Lan66]
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Section 1.5
Chapter 1 · A Scalable Language
Scala is also not the first language to integrate functional and objectoriented programming, although it probably goes furthest in this direction.
Other languages that have integrated some elements of functional programming into object-oriented programming (OOP) include Ruby, Smalltalk, and
Python. On the Java platform, Pizza, Nice, Multi-Java—and Java 8 itself—
have all extended a Java-like core with functional ideas. There are also primarily functional languages that have acquired an object system; examples
are OCaml, F#, and PLT-Scheme.
Scala has also contributed some innovations to the field of programming
languages. For instance, its abstract types provide a more object-oriented
alternative to generic types, its traits allow for flexible component assembly,
and its extractors provide a representation-independent way to do pattern
matching. These innovations have been presented in papers at programming
language conferences in recent years.18
1.5
Conclusion
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In this chapter, we gave you a glimpse of what Scala is and how it might help
you in your programming. To be sure, Scala is not a silver bullet that will
magically make you more productive. To advance, you will need to apply
Scala artfully, and that will require some learning and practice. If you’re
coming to Scala from Java, the most challenging aspects of learning Scala
may involve Scala’s type system (which is richer than Java’s) and its support
for functional programming. The goal of this book is to guide you gently up
Scala’s learning curve, one step at a time. We think you’ll find it a rewarding
intellectual experience that will expand your horizons and make you think
differently about program design. Hopefully, you will also gain pleasure and
inspiration from programming in Scala.
In the next chapter, we’ll get you started writing some Scala code.
18 For
more information, see [Ode03], [Ode05], and [Emi07] in the bibliography.
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63
Chapter 2
First Steps in Scala
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It’s time to write some Scala code. Before we start on the in-depth Scala
tutorial, we put in two chapters that will give you the big picture of Scala,
and most importantly, get you writing code. We encourage you to actually
try out all the code examples presented in this chapter and the next as you
go. The best way to start learning Scala is to program in it.
To run the examples in this chapter, you should have a standard Scala
installation. To get one, go to http://www.scala-lang.org/downloads
and follow the directions for your platform. You can also use a Scala plug-in
for Eclipse, IntelliJ, or NetBeans. For the steps in this chapter, we’ll assume
you’re using the Scala distribution from scala-lang.org.1
If you are a veteran programmer new to Scala, the next two chapters
should give you enough understanding to enable you to start writing useful
programs in Scala. If you are less experienced, some of the material may
seem a bit mysterious to you. But don’t worry. To get you up to speed
quickly, we had to leave out some details. Everything will be explained in a
less “fire hose” fashion in later chapters. In addition, we inserted quite a few
footnotes in these next two chapters to point you to later sections of the book
where you’ll find more detailed explanations.
Step 1. Learn to use the Scala interpreter
The easiest way to get started with Scala is by using the Scala interpreter,
an interactive “shell” for writing Scala expressions and programs. The inter1 We
tested the examples in this book with Scala version 2.11.7.
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Step 1
Chapter 2 · First Steps in Scala
preter, which is called scala, will evaluate expressions you type and print
the resulting value. You use it by typing scala at a command prompt:2
$ scala
Welcome to Scala version 2.11.7
Type in expressions to have them evaluated.
Type :help for more information.
scala>
After you type an expression, such as 1 + 2, and hit enter:
scala> 1 + 2
The interpreter will print:
res0: Int = 3
This line includes:
• an automatically generated or user-defined name to refer to the computed value (res0, which means result 0),
• a colon (:), followed by the type of the expression (Int),
• an equals sign (=),
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• the value resulting from evaluating the expression (3).
The type Int names the class Int in the package scala. Packages in
Scala are similar to packages in Java: They partition the global namespace
and provide a mechanism for information hiding.3 Values of class Int correspond to Java’s int values. More generally, all of Java’s primitive types have
corresponding classes in the scala package. For example, scala.Boolean
corresponds to Java’s boolean. scala.Float corresponds to Java’s float.
And when you compile your Scala code to Java bytecodes, the Scala compiler will use Java’s primitive types where possible to give you the performance benefits of the primitive types.
2 If you’re using Windows, you’ll need to type the scala command into the “Command
Prompt” DOS box.
3 If you’re not familiar with Java packages, you can think of them as providing a full
name for classes. Because Int is a member of package scala, “Int” is the class’s simple
name, and “scala.Int” is its full name. The details of packages are explained in Chapter 13.
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Step 2
Chapter 2 · First Steps in Scala
The resX identifier may be used in later lines. For instance, since res0
was set to 3 previously, res0 * 3 will be 9:
scala> res0 * 3
res1: Int = 9
To print the necessary, but not sufficient, Hello, world! greeting, type:
scala> println("Hello, world!")
Hello, world!
The println function prints the passed string to the standard output, similar
to System.out.println in Java.
Step 2. Define some variables
Scala has two kinds of variables, vals and vars. A val is similar to a final
variable in Java. Once initialized, a val can never be reassigned. A var, by
contrast, is similar to a non-final variable in Java. A var can be reassigned
throughout its lifetime. Here’s a val definition:
scala> val msg = "Hello, world!"
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msg: String = Hello, world!
This statement introduces msg as a name for the string "Hello, world!".
The type of msg is java.lang.String, because Scala strings are implemented by Java’s String class.
If you’re used to declaring variables in Java, you’ll notice one striking
difference here: neither java.lang.String nor String appear anywhere
in the val definition. This example illustrates type inference, Scala’s ability
to figure out types you leave off. In this case, because you initialized msg
with a string literal, Scala inferred the type of msg to be String. When the
Scala interpreter (or compiler) can infer types, it is often best to let it do
so rather than fill the code with unnecessary, explicit type annotations. You
can, however, specify a type explicitly if you wish, and sometimes you probably should. An explicit type annotation can both ensure the Scala compiler
infers the type you intend, as well as serve as useful documentation for future readers of the code. In contrast to Java, where you specify a variable’s
type before its name, in Scala you specify a variable’s type after its name,
separated by a colon. For example:
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Step 2
Chapter 2 · First Steps in Scala
scala> val msg2: java.lang.String = "Hello again, world!"
msg2: String = Hello again, world!
Or, since java.lang types are visible with their simple names4 in Scala
programs, simply:
scala> val msg3: String = "Hello yet again, world!"
msg3: String = Hello yet again, world!
Going back to the original msg, now that it is defined, you can use it as you’d
expect, for example:
scala> println(msg)
Hello, world!
What you can’t do with msg, given that it is a val, not a var, is reassign
it.5 For example, see how the interpreter complains when you attempt the
following:
scala> msg = "Goodbye cruel world!"
:8: error: reassignment to val
msg = "Goodbye cruel world!"
ˆ
If reassignment is what you want, you’ll need to use a var, as in:
scala> var greeting = "Hello, world!"
greeting: String = Hello, world!
Since greeting is a var not a val, you can reassign it later. If you are
feeling grouchy later, for example, you could change your greeting to:
scala> greeting = "Leave me alone, world!"
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greeting: String = Leave me alone, world!
To enter something into the interpreter that spans multiple lines, just keep
typing after the first line. If the code you typed so far is not complete, the
interpreter will respond with a vertical bar on the next line.
4 The
simple name of java.lang.String is String.
however, you can define a new val with a name that was already used
before. This mechanism is explained in Section 7.7.
5 In the interpreter,
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Step 3
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scala> val multiLine =
|
"This is the next line."
multiLine: String = This is the next line.
If you realize you have typed something wrong, but the interpreter is still
waiting for more input, you can escape by pressing enter twice:
scala> val oops =
|
|
You typed two blank lines.
Starting a new command.
scala>
In the rest of the book, we’ll leave out the vertical bars to make the code
easier to read (and easier to copy and paste from the PDF eBook into the
interpreter).
Step 3. Define some functions
Now that you’ve worked with Scala variables, you’ll probably want to write
some functions. Here’s how you do that in Scala:
scala> def max(x: Int, y: Int): Int = {
if (x > y) x
else y
}
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max: (x: Int, y: Int)Int
Function definitions start with def. The function’s name, in this case max, is
followed by a comma-separated list of parameters in parentheses. A type annotation must follow every function parameter, preceded by a colon, because
the Scala compiler (and interpreter, but from now on we’ll just say compiler)
does not infer function parameter types. In this example, the function named
max takes two parameters, x and y, both of type Int. After the close parenthesis of max’s parameter list you’ll find another “: Int” type annotation.
This one defines the result type of the max function itself.6 Following the
6 In Java, the type of the value returned from a method is its return type. In Scala, that
same concept is called result type.
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“def” starts a function definition
function name
parameter list in parentheses
function’s result type
equals sign
def max(x: Int, y: Int): Int = {
if (x > y)
x
function body
else
in curly braces
y
}
Figure 2.1 · The basic form of a function definition in Scala.
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function’s result type is an equals sign and pair of curly braces that contain
the body of the function. In this case, the body contains a single if expression, which selects either x or y, whichever is greater, as the result of the
max function. As demonstrated here, Scala’s if expression can result in a
value, similar to Java’s ternary operator. For example, the Scala expression
“if (x > y) x else y” behaves similarly to “(x > y) ? x : y” in Java. The
equals sign that precedes the body of a function hints that in the functional
world view, a function defines an expression that results in a value. The basic
structure of a function is illustrated in Figure 2.1.
Sometimes the Scala compiler will require you to specify the result type
of a function. If the function is recursive,7 for example, you must explicitly
specify the function’s result type. In the case of max, however, you may leave
the result type off and the compiler will infer it.8 Also, if a function consists
of just one statement, you can optionally leave off the curly braces. Thus,
you could alternatively write the max function like this:
scala> def max(x: Int, y: Int) = if (x > y) x else y
max: (x: Int, y: Int)Int
7A
function is recursive if it calls itself.
it is often a good idea to indicate function result types explicitly, even
when the compiler doesn’t require it. Such type annotations can make the code easier to read,
because the reader need not study the function body to figure out the inferred result type.
8 Nevertheless,
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Chapter 2 · First Steps in Scala
Once you have defined a function, you can call it by name, as in:
scala> max(3, 5)
res4: Int = 5
Here’s the definition of a function that takes no parameters and returns no
interesting result:
scala> def greet() = println("Hello, world!")
greet: ()Unit
When you define the greet() function, the interpreter will respond with
greet: ()Unit. “greet” is, of course, the name of the function. The empty
parentheses indicate the function takes no parameters. And Unit is greet’s
result type. A result type of Unit indicates the function returns no interesting
value. Scala’s Unit type is similar to Java’s void type; in fact, every voidreturning method in Java is mapped to a Unit-returning method in Scala.
Methods with the result type of Unit, therefore, are only executed for their
side effects. In the case of greet(), the side effect is a friendly greeting
printed to the standard output.
In the next step, you’ll place Scala code in a file and run it as a script. If
you wish to exit the interpreter, you can do so by entering :quit or :q.
scala> :quit
$
Step 4. Write some Scala scripts
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Although Scala is designed to help programmers build very large-scale systems, it also scales down nicely to scripting. A script is just a sequence of
statements in a file that will be executed sequentially. Put this into a file
named hello.scala:
println("Hello, world, from a script!")
then run:9
9 You can run scripts without typing “scala” on Unix and Windows using a “poundbang” syntax, which is shown in Appendix A.
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Step 5
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$ scala hello.scala
And you should get yet another greeting:
Hello, world, from a script!
Command line arguments to a Scala script are available via a Scala array
named args. In Scala, arrays are zero based, and you access an element
by specifying an index in parentheses. So the first element in a Scala array
named steps is steps(0), not steps[0], as in Java. To try this out, type
the following into a new file named helloarg.scala:
// Say hello to the first argument
println("Hello, " + args(0) + "!")
then run:
$ scala helloarg.scala planet
In this command, "planet" is passed as a command line argument, which
is accessed in the script as args(0). Thus, you should see:
Hello, planet!
Note that this script included a comment. The Scala compiler will ignore
characters between // and the next end of line and any characters between /*
and */. This example also shows Strings being concatenated with the + operator. This works as you’d expect. The expression "Hello, " + "world!"
will result in the string "Hello, world!".
Step 5. Loop with while; decide with if
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To try out a while, type the following into a file named printargs.scala:
var i = 0
while (i < args.length) {
println(args(i))
i += 1
}
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Step 5
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Note
Although the examples in this section help explain while loops, they do
not demonstrate the best Scala style. In the next section, you’ll see better
approaches that avoid iterating through arrays with indexes.
This script starts with a variable definition, var i = 0. Type inference
gives i the type scala.Int, because that is the type of its initial value, 0.
The while construct on the next line causes the block (the code between
the curly braces) to be repeatedly executed until the boolean expression
i < args.length is false. args.length gives the length of the args array.
The block contains two statements, each indented two spaces, the recommended indentation style for Scala. The first statement, println(args(i)),
prints out the ith command line argument. The second statement, i += 1, increments i by one. Note that Java’s ++i and i++ don’t work in Scala. To
increment in Scala, you need to say either i = i + 1 or i += 1. Run this script
with the following command:
$ scala printargs.scala Scala is fun
And you should see:
Scala
is
fun
For even more fun, type the following code into a new file with the name
echoargs.scala:
var i = 0
while (i < args.length) {
if (i != 0)
print(" ")
print(args(i))
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i += 1
}
println()
In this version, you’ve replaced the println call with a print call, so that
all the arguments will be printed out on the same line. To make this readable,
you’ve inserted a single space before each argument except the first via the
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Step 6
Chapter 2 · First Steps in Scala
if (i != 0) construct. Since i != 0 will be false the first time through
the while loop, no space will get printed before the initial argument. Lastly,
you’ve added one more println to the end, to get a line return after printing
out all the arguments. Your output will be very pretty indeed. If you run this
script with the following command:
$ scala echoargs.scala Scala is even more fun
You’ll get:
Scala is even more fun
Note that in Scala, as in Java, you must put the boolean expression for
a while or an if in parentheses. (In other words, you can’t say in Scala
things like if i < 10 as you can in a language such as Ruby. You must say
if (i < 10) in Scala.) Another similarity to Java is that if a block has only
one statement, you can optionally leave off the curly braces, as demonstrated
by the if statement in echoargs.scala. And although you haven’t seen any
of them, Scala does use semicolons to separate statements as in Java, except
that in Scala the semicolons are very often optional, giving some welcome
relief to your right little finger. If you had been in a more verbose mood,
therefore, you could have written the echoargs.scala script as follows:
var i = 0;
while (i < args.length) {
if (i != 0) {
print(" ");
}
print(args(i));
i += 1;
}
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println();
Step 6. Iterate with foreach and for
Although you may not have realized it, when you wrote the while loops in
the previous step, you were programming in an imperative style. In the imperative style, which is the style you normally use with languages like Java,
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Step 6
Chapter 2 · First Steps in Scala
C++, and C, you give one imperative command at a time, iterate with loops,
and often mutate state shared between different functions. Scala enables you
to program imperatively, but as you get to know Scala better, you’ll likely
often find yourself programming in a more functional style. In fact, one of
the main aims of this book is to help you become as comfortable with the
functional style as you are with imperative style.
One of the main characteristics of a functional language is that functions
are first class constructs, and that’s very true in Scala. For example, another
(far more concise) way to print each command line argument is:
args.foreach(arg => println(arg))
In this code, you call the foreach method on args and pass in a function. In
this case, you’re passing in a function literal that takes one parameter named
arg. The body of the function is println(arg). If you type the above code
into a new file named pa.scala and execute with the command:
$ scala pa.scala Concise is nice
You should see:
Concise
is
nice
In the previous example, the Scala interpreter infers the type of arg to
be String, since String is the element type of the array on which you’re
calling foreach. If you’d prefer to be more explicit, you can mention the
type name. But when you do, you’ll need to wrap the argument portion in
parentheses (which is the normal form of the syntax anyway):
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args.foreach((arg: String) => println(arg))
Running this script has the same behavior as the previous one.
If you’re in the mood for more conciseness instead of more explicitness,
you can take advantage of a special shorthand in Scala. If a function literal
consists of one statement that takes a single argument, you need not explicitly
name and specify the argument.10 Thus, the following code also works:
10 This
shorthand, called a partially applied function, is described in Section 8.6.
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74
Step 6
Chapter 2 · First Steps in Scala
function
parameters
in parentheses
right
arrow
function
body
(x: Int, y: Int) => x + y
Figure 2.2 · The syntax of a function literal in Scala.
args.foreach(println)
To summarize, the syntax for a function literal is a list of named parameters,
in parentheses, a right arrow, and then the body of the function. This syntax
is illustrated in Figure 2.2.
Now, by this point you may be wondering what happened to those trusty
for loops you have been accustomed to using in imperative languages, such
as Java or C. In an effort to guide you in a functional direction, only a functional relative of the imperative for (called a for expression) is available in
Scala. While you won’t see their full power and expressiveness until you
reach (or peek ahead to) Section 7.3, we’ll give you a glimpse here. In a new
file named forargs.scala, type the following:
for (arg <- args)
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println(arg)
The parentheses after the “for” contain arg <- args.11 To the right of
the <- symbol is the familiar args array. To the left of <- is “arg”, the name
of a val, not a var. (Because it is always a val, you just write “arg” by
itself, not “val arg”.) Although arg may seem to be a var, because it will
get a new value on each iteration, it really is a val: arg can’t be reassigned
inside the body of the for expression. Instead, for each element of the args
array, a new arg val will be created and initialized to the element value, and
the body of the for will be executed.
If you run the forargs.scala script with the command:
11 You
can say “in” for the <- symbol. You’d read for (arg <- args), therefore, as “for
arg in args.”
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Conclusion
Chapter 2 · First Steps in Scala
$ scala forargs.scala for arg in args
You’ll see:
for
arg
in
args
Scala’s for expression can do much more than this, but this example is
enough to get you started. We’ll show you more about for in Section 7.3
and Chapter 23.
Conclusion
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In this chapter, you learned some Scala basics and, hopefully, took advantage
of the opportunity to write a bit of Scala code. In the next chapter, we’ll
continue this introductory overview and get into more advanced topics.
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Chapter 3
Next Steps in Scala
This chapter continues the previous chapter’s introduction to Scala. In this
chapter, we’ll introduce some more advanced features. When you complete
this chapter, you should have enough knowledge to enable you to start writing useful scripts in Scala. As with the previous chapter, we recommend you
try out these examples as you go. The best way to get a feel for Scala is to
start writing Scala code.
Step 7. Parameterize arrays with types
In Scala, you can instantiate objects, or class instances, using new. When
you instantiate an object in Scala, you can parameterize it with values and
types. Parameterization means “configuring” an instance when you create it.
You parameterize an instance with values by passing objects to a constructor
in parentheses. For example, the following Scala code instantiates a new
java.math.BigInteger and parameterizes it with the value "12345":
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val big = new java.math.BigInteger("12345")
You parameterize an instance with types by specifying one or more types
in square brackets. An example is shown in Listing 3.1. In this example,
greetStrings is a value of type Array[String] (an “array of string”) that
is initialized to length 3 by parameterizing it with the value 3 in the first line
of code. If you run the code in Listing 3.1 as a script, you’ll see yet another
Hello, world! greeting. Note that when you parameterize an instance with
both a type and a value, the type comes first in its square brackets, followed
by the value in parentheses.
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Step 7
Chapter 3 · Next Steps in Scala
val greetStrings = new Array[String](3)
greetStrings(0) = "Hello"
greetStrings(1) = ", "
greetStrings(2) = "world!\n"
for (i <- 0 to 2)
print(greetStrings(i))
Listing 3.1 · Parameterizing an array with a type.
Note
Although the code in Listing 3.1 demonstrates important concepts, it does
not show the recommended way to create and initialize an array in Scala.
You’ll see a better way in Listing 3.2 on page 81.
Had you been in a more explicit mood, you could have specified the type
of greetStrings explicitly like this:
val greetStrings: Array[String] = new Array[String](3)
Given Scala’s type inference, this line of code is semantically equivalent to
the actual first line of Listing 3.1. But this form demonstrates that while
the type parameterization portion (the type names in square brackets) forms
part of the type of the instance, the value parameterization part (the values in
parentheses) does not. The type of greetStrings is Array[String], not
Array[String](3).
The next three lines of code in Listing 3.1 initialize each element of the
greetStrings array:
greetStrings(0) = "Hello"
greetStrings(1) = ", "
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greetStrings(2) = "world!\n"
As mentioned previously, arrays in Scala are accessed by placing the index
inside parentheses, not square brackets as in Java. Thus the zeroth element
of the array is greetStrings(0), not greetStrings[0].
These three lines of code illustrate an important concept to understand
about Scala concerning the meaning of val. When you define a variable
with val, the variable can’t be reassigned, but the object to which it refers
could potentially still be changed. So in this case, you couldn’t reassign
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Step 7
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greetStrings to a different array; greetStrings will always point to the
same Array[String] instance with which it was initialized. But you can
change the elements of that Array[String] over time, so the array itself is
mutable.
The final two lines in Listing 3.1 contain a for expression that prints out
each greetStrings array element in turn:
for (i <- 0 to 2)
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print(greetStrings(i))
The first line of code in this for expression illustrates another general rule
of Scala: if a method takes only one parameter, you can call it without a
dot or parentheses. The to in this example is actually a method that takes
one Int argument. The code 0 to 2 is transformed into the method call
(0).to(2).1 Note that this syntax only works if you explicitly specify the
receiver of the method call. You cannot write “println 10”, but you can
write “Console println 10”.
Scala doesn’t technically have operator overloading, because it doesn’t
actually have operators in the traditional sense. Instead, characters such as
+, -, *, and / can be used in method names. Thus, when you typed 1 + 2
into the Scala interpreter in Step 1, you were actually invoking a method
named + on the Int object 1, passing in 2 as a parameter. As illustrated
in Figure 3.1, you could alternatively have written 1 + 2 using traditional
method invocation syntax, (1).+(2).
Another important idea illustrated by this example will give you insight
into why arrays are accessed with parentheses in Scala. Scala has fewer
special cases than Java. Arrays are simply instances of classes like any other
class in Scala. When you apply parentheses surrounding one or more values
to a variable, Scala will transform the code into an invocation of a method
named apply on that variable. So greetStrings(i) gets transformed into
greetStrings.apply(i). Thus accessing an element of an array in Scala
is simply a method call like any other. This principle is not restricted to
arrays: any application of an object to some arguments in parentheses will
be transformed to an apply method call. Of course this will compile only
1 This to method actually returns not an array but a different kind of sequence, containing
the values 0, 1, and 2, which the for expression iterates over. Sequences and other collections
will be described in Chapter 17.
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79
Step 7
Chapter 3 · Next Steps in Scala
1 + 2
Int object
with value 1
invoking on 1
a method
named ‘+’
Passing the Int
object 2 to the
‘+’ method
(1).+(2)
Figure 3.1 · All operations are method calls in Scala.
if that type of object actually defines an apply method. So it’s not a special
case; it’s a general rule.
Similarly, when an assignment is made to a variable to which parentheses
and one or more arguments have been applied, the compiler will transform
that into an invocation of an update method that takes the arguments in
parentheses as well as the object to the right of the equals sign. For example:
greetStrings(0) = "Hello"
will be transformed into:
greetStrings.update(0, "Hello")
Thus, the following is semantically equivalent to the code in Listing 3.1:
val greetStrings = new Array[String](3)
greetStrings.update(0, "Hello")
greetStrings.update(1, ", ")
greetStrings.update(2, "world!\n")
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for (i <- 0.to(2))
print(greetStrings.apply(i))
Scala achieves a conceptual simplicity by treating everything, from arrays to expressions, as objects with methods. You don’t have to remember
special cases, such as the differences in Java between primitive and their corresponding wrapper types, or between arrays and regular objects. Moreover,
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80
Step 8
Chapter 3 · Next Steps in Scala
this uniformity does not incur a significant performance cost. The Scala compiler uses Java arrays, primitive types, and native arithmetic where possible
in the compiled code.
Although the examples you’ve seen so far in this step compile and run
just fine, Scala provides a more concise way to create and initialize arrays
that you would normally use (see Listing 3.2). This code creates a new array
of length three, initialized to the passed strings, "zero", "one", and "two".
The compiler infers the type of the array to be Array[String], because you
passed strings to it.
val numNames = Array("zero", "one", "two")
Listing 3.2 · Creating and initializing an array.
What you’re actually doing in Listing 3.2 is calling a factory method,
named apply, which creates and returns the new array. This apply method
takes a variable number of arguments2 and is defined on the Array companion object. You’ll learn more about companion objects in Section 4.3. If
you’re a Java programmer, you can think of this as calling a static method
named apply on class Array. A more verbose way to call the same apply
method is:
val numNames2 = Array.apply("zero", "one", "two")
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Step 8. Use lists
One of the big ideas of the functional style of programming is that methods
should not have side effects. A method’s only act should be to compute and
return a value. Some benefits gained when you take this approach are that
methods become less entangled, and therefore more reliable and reusable.
Another benefit (in a statically typed language) is that everything that goes
into and out of a method is checked by a type checker, so logic errors are
more likely to manifest themselves as type errors. Applying this functional
philosophy to the world of objects means making objects immutable.
As you’ve seen, a Scala array is a mutable sequence of objects that all
share the same type. An Array[String] contains only strings, for example.
2 Variable-length
argument lists, or repeated parameters, are described in Section 8.8.
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81
Step 8
Chapter 3 · Next Steps in Scala
Although you can’t change the length of an array after it is instantiated, you
can change its element values. Thus, arrays are mutable objects.
For an immutable sequence of objects that share the same type you can
use Scala’s List class. As with arrays, a List[String] contains only
strings. Scala’s List, scala.List, differs from Java’s java.util.List
type in that Scala Lists are always immutable (whereas Java Lists can be
mutable). More generally, Scala’s List is designed to enable a functional
style of programming. Creating a list is easy, and Listing 3.3 shows how:
val oneTwoThree = List(1, 2, 3)
Listing 3.3 · Creating and initializing a list.
The code in Listing 3.3 establishes a new val named oneTwoThree, initialized with a new List[Int] with the integer elements 1, 2, and 3.3 Because Lists are immutable, they behave a bit like Java strings: when you call
a method on a list that might seem by its name to imply the list will mutate, it
instead creates and returns a new list with the new value. For example, List
has a method named ‘:::’ for list concatenation. Here’s how you use it:
val oneTwo = List(1, 2)
val threeFour = List(3, 4)
val oneTwoThreeFour = oneTwo ::: threeFour
println(oneTwo + " and " + threeFour + " were not mutated.")
println("Thus, " + oneTwoThreeFour + " is a new list.")
If you run this script, you’ll see:
List(1, 2) and List(3, 4) were not mutated.
Thus, List(1, 2, 3, 4) is a new list.
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Perhaps the most common operator you’ll use with lists is ‘::’, which
is pronounced “cons.” Cons prepends a new element to the beginning of an
existing list and returns the resulting list. For example, if you run this script:
val twoThree = List(2, 3)
val oneTwoThree = 1 :: twoThree
println(oneTwoThree)
3 You don’t need to say new List because “List.apply()” is defined as a factory method
on the scala.List companion object. You’ll read more on companion objects in Section 4.3.
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82
Step 8
Chapter 3 · Next Steps in Scala
You’ll see:
List(1, 2, 3)
Note
In the expression “1 :: twoThree”, :: is a method of its right operand,
the list, twoThree. You might suspect there’s something amiss with the
associativity of the :: method, but it is actually a simple rule to
remember: If a method is used in operator notation, such as a * b, the
method is invoked on the left operand, as in a.*(b)—unless the method
name ends in a colon. If the method name ends in a colon, the method is
invoked on the right operand. Therefore, in 1 :: twoThree, the :: method
is invoked on twoThree, passing in 1, like this: twoThree.::(1).
Operator associativity will be described in more detail in Section 5.9.
Given that a shorthand way to specify an empty list is Nil, one way to
initialize new lists is to string together elements with the cons operator, with
Nil as the last element.4 For example, the following script will produce the
same output as the previous one, “List(1, 2, 3)”:
val oneTwoThree = 1 :: 2 :: 3 :: Nil
println(oneTwoThree)
Scala’s List is packed with useful methods, many of which are shown in
Table 3.1. The full power of lists will be revealed in Chapter 16.
Why not append to lists?
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Class List does offer an “append” operation—it’s written :+ and is
explained in Chapter 24—but this operation is rarely used, because
the time it takes to append to a list grows linearly with the size of the
list, whereas prepending with :: takes constant time. If you want to
build a list efficiently by appending elements, you can prepend them
and when you’re done call reverse. Or you can use a ListBuffer, a
mutable list that does offer an append operation, and when you’re done
call toList. ListBuffer will be described in Section 22.2.
4 The
reason you need Nil at the end is that :: is defined on class List. If you try to just
say 1 :: 2 :: 3, it won’t compile because 3 is an Int, which doesn’t have a :: method.
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83
Step 8
Chapter 3 · Next Steps in Scala
Table 3.1 · Some List methods and usages
What it is
What it does
List() or Nil
The empty List
List("Cool", "tools", "rule")
Creates a new List[String] with the
three values "Cool", "tools", and
"rule"
val thrill = "Will" :: "fill" ::
"until" :: Nil
Creates a new List[String] with the
three values "Will", "fill", and
"until"
List("a", "b") ::: List("c", "d")
Concatenates two lists (returns a new
List[String] with values "a", "b",
"c", and "d")
thrill(2)
Returns the element at index 2 (zero
based) of the thrill list (returns
"until")
thrill.count(s => s.length == 4)
Counts the number of string elements in
thrill that have length 4 (returns 2)
thrill.drop(2)
Returns the thrill list without its first 2
elements (returns List("until"))
thrill.dropRight(2)
thrill.exists(s => s == "until")
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thrill.filter(s => s.length == 4)
thrill.forall(s =>
s.endsWith("l"))
thrill.foreach(s => print(s))
Returns the thrill list without its
rightmost 2 elements (returns
List("Will"))
Determines whether a string element
exists in thrill that has the value
"until" (returns true)
Returns a list of all elements, in order, of
the thrill list that have length 4 (returns
List("Will", "fill"))
Indicates whether all elements in the
thrill list end with the letter "l"
(returns true)
Executes the print statement on each of
the strings in the thrill list (prints
"Willfilluntil")
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84
Step 8
Chapter 3 · Next Steps in Scala
Table 3.1 · continued
thrill.foreach(print)
Same as the previous, but more concise
(also prints "Willfilluntil")
thrill.head
Returns the first element in the thrill
list (returns "Will")
thrill.init
Returns a list of all but the last element in
the thrill list (returns
List("Will", "fill"))
thrill.isEmpty
thrill.last
Returns the last element in the thrill
list (returns "until")
thrill.length
Returns the number of elements in the
thrill list (returns 3)
thrill.map(s => s + "y")
thrill.mkString(", ")
thrill.filterNot(s =>
s.length == 4)
thrill.reverse
thrill.sort((s, t) =>
s.charAt(0).toLower <
t.charAt(0).toLower)
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Indicates whether the thrill list is
empty (returns false)
thrill.tail
Returns a list resulting from adding a "y"
to each string element in the thrill list
(returns
List("Willy", "filly", "untily"))
Makes a string with the elements of the
list (returns "Will, fill, until")
Returns a list of all elements, in order, of
the thrill list except those that have
length 4 (returns List("until"))
Returns a list containing all elements of
the thrill list in reverse order (returns
List("until", "fill", "Will"))
Returns a list containing all elements of
the thrill list in alphabetical order of
the first character lowercased (returns
List("fill", "until", "Will"))
Returns the thrill list minus its first
element (returns
List("fill", "until"))
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Step 9
Chapter 3 · Next Steps in Scala
Step 9. Use tuples
Another useful container object is the tuple. Like lists, tuples are immutable,
but unlike lists, tuples can contain different types of elements. Whereas a
list might be a List[Int] or a List[String], a tuple could contain both
an integer and a string at the same time. Tuples are very useful, for example,
if you need to return multiple objects from a method. Whereas in Java you
would often create a JavaBean-like class to hold the multiple return values,
in Scala you can simply return a tuple. And it is simple: To instantiate a new
tuple that holds some objects, just place the objects in parentheses, separated
by commas. Once you have a tuple instantiated, you can access its elements
individually with a dot, underscore, and the one-based index of the element.
An example is shown in Listing 3.4:
val pair = (99, "Luftballons")
println(pair._1)
println(pair._2)
Listing 3.4 · Creating and using a tuple.
In the first line of Listing 3.4, you create a new tuple that contains the
integer 99, as its first element, and the string, "Luftballons", as its second
element. Scala infers the type of the tuple to be Tuple2[Int, String], and
gives that type to the variable pair as well. In the second line, you access
the _1 field, which will produce the first element, 99. The “.” in the second
line is the same dot you’d use to access a field or invoke a method. In this
case you are accessing a field named _1. If you run this script, you’ll see:
99
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Luftballons
The actual type of a tuple depends on the number of elements it contains
and the types of those elements. Thus, the type of (99, "Luftballons")
is Tuple2[Int, String]. The type of ('u', 'r', "the", 1, 4, "me") is
Tuple6[Char, Char, String, Int, Int, String].5
5 Although conceptually you could create tuples of any length, currently the Scala library
only defines them up to Tuple22.
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Step 10
Chapter 3 · Next Steps in Scala
Accessing the elements of a tuple
You may be wondering why you can’t access the elements of a tuple
like the elements of a list, for example, with “pair(0)”. The reason
is that a list’s apply method always returns the same type, but each
element of a tuple may be a different type: _1 can have one result type,
_2 another, and so on. These _N numbers are one-based, instead of
zero-based, because starting with 1 is a tradition set by other languages
with statically typed tuples, such as Haskell and ML.
Step 10. Use sets and maps
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Because Scala aims to help you take advantage of both functional and imperative styles, its collections libraries make a point to differentiate between
mutable and immutable collections. For example, arrays are always mutable; lists are always immutable. Scala also provides mutable and immutable
alternatives for sets and maps, but uses the same simple names for both versions. For sets and maps, Scala models mutability in the class hierarchy.
For example, the Scala API contains a base trait for sets, where a trait
is similar to a Java interface. (You’ll find out more about traits in Chapter 12.) Scala then provides two subtraits, one for mutable sets and another
for immutable sets.
As you can see in Figure 3.2, these three traits all share the same simple name, Set. Their fully qualified names differ, however, because each
resides in a different package. Concrete set classes in the Scala API, such
as the HashSet classes shown in Figure 3.2, extend either the mutable or
immutable Set trait. (Although in Java you “implement” interfaces, in Scala
you “extend” or “mix in” traits.) Thus, if you want to use a HashSet, you
can choose between mutable and immutable varieties depending upon your
needs. The default way to create a set is shown in Listing 3.5:
var jetSet = Set("Boeing", "Airbus")
jetSet += "Lear"
println(jetSet.contains("Cessna"))
Listing 3.5 · Creating, initializing, and using an immutable set.
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scala.collection
Set
«trait»
scala.collection.immutable
scala.collection.mutable
«trait»
«trait»
scala.collection.immutable
scala.collection.mutable
Set
HashSet
Set
HashSet
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0B8GZ05FEi
Figure 3.2 · Class hierarchy for Scala sets.
In the first line of code in Listing 3.5, you define a new var named
jetSet and initialize it with an immutable set containing the two strings,
"Boeing" and "Airbus". As this example shows, you can create sets in
Scala similarly to how you create lists and arrays: by invoking a factory
method named apply on a Set companion object. In Listing 3.5, you invoke
apply on the companion object for scala.collection.immutable.Set,
which returns an instance of a default, immutable Set. The Scala compiler
infers jetSet’s type to be the immutable Set[String].
To add a new element to a set, you call + on the set, passing in the new
element. On both mutable and immutable sets, the + method will create and
return a new set with the element added. In Listing 3.5, you’re working
with an immutable set. Although mutable sets offer an actual += method,
immutable sets do not.
In this case, the second line of code, “jetSet += "Lear"”, is essentially
a shorthand for:
jetSet = jetSet + "Lear"
Thus, in the second line of Listing 3.5, you reassign the jetSet var with a
new set containing "Boeing", "Airbus", and "Lear". Finally, the last line
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of Listing 3.5 prints out whether or not the set contains the string "Cessna".
(As you’d expect, it prints false.)
If you want a mutable set, you’ll need to use an import, as shown in
Listing 3.6:
import scala.collection.mutable
val movieSet = mutable.Set("Hitch", "Poltergeist")
movieSet += "Shrek"
println(movieSet)
Listing 3.6 · Creating, initializing, and using a mutable set.
In the first line of Listing 3.6 you import the mutable Set. As with Java,
an import statement allows you to use a simple name, such as Set, instead of
the longer, fully qualified name. As a result, when you say Set on the third
line, the compiler knows you mean scala.collection.mutable.Set. On
that line, you initialize movieSet with a new mutable set that contains the
strings "Hitch" and "Poltergeist". The subsequent line adds "Shrek"
to the mutable set by calling the += method on the set, passing in the string
"Shrek". As mentioned previously, += is an actual method defined on mutable sets. Had you wanted to, instead of writing movieSet += "Shrek", you
could have written movieSet.+=("Shrek").6
Although the default set implementations produced by the mutable and
immutable Set factory methods shown thus far will likely be sufficient for
most situations, occasionally you may want an explicit set class. Fortunately,
the syntax is similar. Simply import that class you need, and use the factory
method on its companion object. For example, if you need an immutable
HashSet, you could do this:
import scala.collection.immutable.HashSet
val hashSet = HashSet("Tomatoes", "Chilies")
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println(hashSet + "Coriander")
Another useful collection class in Scala is Map. As with sets, Scala provides mutable and immutable versions of Map, using a class hierarchy. As
6 Because
the set in Listing 3.6 is mutable, there is no need to reassign movieSet, which
is why it can be a val. By contrast, using += with the immutable set in Listing 3.5 required
reassigning jetSet, which is why it must be a var.
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scala.collection
Map
«trait»
scala.collection.immutable
scala.collection.mutable
«trait»
«trait»
scala.collection.immutable
scala.collection.mutable
Map
HashMap
Map
HashMap
Figure 3.3 · Class hierarchy for Scala maps.
you can see in Figure 3.3, the class hierarchy for maps looks a lot like the
one for sets. There’s a base Map trait in package scala.collection, and
two subtrait Maps: a mutable Map in scala.collection.mutable and an
immutable one in scala.collection.immutable.
Implementations of Map, such as the HashMaps shown in the class hierarchy in Figure 3.3, extend either the mutable or immutable trait. You can
create and initialize maps using factory methods similar to those used for
arrays, lists, and sets.
import scala.collection.mutable
val treasureMap = mutable.Map[Int, String]()
treasureMap += (1 -> "Go to island.")
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treasureMap += (2 -> "Find big X on ground.")
treasureMap += (3 -> "Dig.")
println(treasureMap(2))
Listing 3.7 · Creating, initializing, and using a mutable map.
For example, Listing 3.7 shows a mutable map in action. On the first
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line of Listing 3.7, you import the mutable Map. You then define a val
named treasureMap, and initialize it with an empty mutable Map that has
integer keys and string values. The map is empty because you pass nothing to the factory method (the parentheses in “Map[Int, String]()” are
empty).7 On the next three lines you add key/value pairs to the map using the -> and += methods. As illustrated previously, the Scala compiler
transforms a binary operation expression like 1 -> "Go to island." into
(1).->("Go to island."). Thus, when you say 1 -> "Go to island.",
you are actually calling a method named -> on an integer with the value 1,
passing in a string with the value "Go to island." This -> method, which
you can invoke on any object in a Scala program, returns a two-element tuple
containing the key and value.8 You then pass this tuple to the += method of
the map object to which treasureMap refers. Finally, the last line prints the
value that corresponds to the key 2 in the treasureMap.
If you run this code, it will print:
Find big X on ground.
If you prefer an immutable map, no import is necessary, as immutable is
the default map. An example is shown in Listing 3.8:
val romanNumeral = Map(
1 -> "I", 2 -> "II", 3 -> "III", 4 -> "IV", 5 -> "V"
)
println(romanNumeral(4))
Listing 3.8 · Creating, initializing, and using an immutable map.
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Given there are no imports, when you say Map in the first line of Listing 3.8, you’ll get the default: a scala.collection.immutable.Map. You
pass five key/value tuples to the map’s factory method, which returns an immutable Map containing the passed key/value pairs. If you run the code in
Listing 3.8 it will print “IV”.
7 The explicit type parameterization, “[Int, String]”, is required in Listing 3.7 because
without any values passed to the factory method, the compiler is unable to infer the map’s
type parameters. By contrast, the compiler can infer the type parameters from the values
passed to the map factory shown in Listing 3.8, thus no explicit type parameters are needed.
8 The Scala mechanism that allows you to invoke -> on any object, implicit conversion,
will be covered in Chapter 21.
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Step 11
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Step 11. Learn to recognize the functional style
As mentioned in Chapter 1, Scala allows you to program in an imperative
style, but encourages you to adopt a more functional style. If you are coming
to Scala from an imperative background—for example, if you are a Java
programmer—one of the main challenges you may face when learning Scala
is figuring out how to program in the functional style. We realize this style
might be unfamiliar at first, and in this book we try hard to guide you through
the transition. It will require some work on your part, and we encourage
you to make the effort. If you come from an imperative background, we
believe that learning to program in a functional style will not only make you
a better Scala programmer, it will expand your horizons and make you a
better programmer in general.
The first step is to recognize the difference between the two styles in
code. One telltale sign is that if code contains any vars, it is probably in
an imperative style. If the code contains no vars at all—i.e., it contains
only vals—it is probably in a functional style. One way to move towards a
functional style, therefore, is to try to program without vars.
If you’re coming from an imperative background, such as Java, C++, or
C#, you may think of var as a regular variable and val as a special kind of
variable. On the other hand, if you’re coming from a functional background,
such as Haskell, OCaml, or Erlang, you might think of val as a regular variable and var as akin to blasphemy. The Scala perspective, however, is that
val and var are just two different tools in your toolbox, both useful, neither
inherently evil. Scala encourages you to lean towards vals, but ultimately
reach for the best tool given the job at hand. Even if you agree with this balanced philosophy, however, you may still find it challenging at first to figure
out how to get rid of vars in your code.
Consider the following while loop example, adapted from Chapter 2,
which uses a var and is therefore in the imperative style:
def printArgs(args: Array[String]): Unit = {
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var i = 0
while (i < args.length) {
println(args(i))
i += 1
}
}
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You can transform this bit of code into a more functional style by getting rid
of the var, for example, like this:
def printArgs(args: Array[String]): Unit = {
for (arg <- args)
println(arg)
}
or this:
def printArgs(args: Array[String]): Unit = {
args.foreach(println)
}
This example illustrates one benefit of programming with fewer vars.
The refactored (more functional) code is clearer, more concise, and less
error-prone than the original (more imperative) code. The reason Scala encourages a functional style is that it can help you write more understandable,
less error-prone code.
But you can go even further. The refactored printArgs method is not
purely functional because it has side effects—in this case, its side effect is
printing to the standard output stream. The telltale sign of a function with
side effects is that its result type is Unit. If a function isn’t returning any
interesting value, which is what a result type of Unit means, the only way
that function can make a difference in the world is through some kind of side
effect. A more functional approach would be to define a method that formats
the passed args for printing, but just returns the formatted string, as shown
in Listing 3.9:
def formatArgs(args: Array[String]) = args.mkString("\n")
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Listing 3.9 · A function without side effects or vars.
Now you’re really functional: no side effects or vars in sight. The
mkString method, which you can call on any iterable collection (including arrays, lists, sets, and maps), returns a string consisting of the result of
calling toString on each element, separated by the passed string. Thus if
args contains three elements "zero", "one", and "two", formatArgs will
return "zero\none\ntwo". Of course, this function doesn’t actually print
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anything out like the printArgs methods did, but you can easily pass its
result to println to accomplish that:
println(formatArgs(args))
Every useful program is likely to have side effects of some form; otherwise, it wouldn’t be able to provide value to the outside world. Preferring
methods without side effects encourages you to design programs where sideeffecting code is minimized. One benefit of this approach is that it can help
make your programs easier to test.
For example, to test any of the three printArgs methods shown earlier
in this section, you’d need to redefine println, capture the output passed
to it, and make sure it is what you expect. By contrast, you could test the
formatArgs function simply by checking its result:
val res = formatArgs(Array("zero", "one", "two"))
assert(res == "zero\none\ntwo")
Scala’s assert method checks the passed Boolean and if it is false, throws
AssertionError. If the passed Boolean is true, assert just returns quietly.
You’ll learn more about assertions and tests in Chapter 14.
That said, bear in mind that neither vars nor side effects are inherently
evil. Scala is not a pure functional language that forces you to program
everything in the functional style. Scala is a hybrid imperative/functional
language. You may find that in some situations an imperative style is a better
fit for the problem at hand, and in such cases you should not hesitate to use
it. To help you learn how to program without vars, however, we’ll show you
many specific examples of code with vars and how to transform those vars
to vals in Chapter 7.
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A balanced attitude for Scala programmers
Prefer vals, immutable objects, and methods without side effects.
Reach for them first. Use vars, mutable objects, and methods with side
effects when you have a specific need and justification for them.
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Step 12
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Step 12. Read lines from a file
Scripts that perform small, everyday tasks often need to process lines in files.
In this section, you’ll build a script that reads lines from a file and prints them
out prepended with the number of characters in each line. The first version
is shown in Listing 3.10:
import scala.io.Source
if (args.length > 0) {
for (line <- Source.fromFile(args(0)).getLines())
println(line.length + " " + line)
}
else
Console.err.println("Please enter filename")
Listing 3.10 · Reading lines from a file.
This script starts with an import of a class named Source from package
scala.io. It then checks to see if at least one argument was specified on
the command line. If so, the first argument is interpreted as a filename to
open and process. The expression Source.fromFile(args(0)) attempts
to open the specified file and returns a Source object, on which you call
getLines. The getLines method returns an Iterator[String], which
provides one line on each iteration, excluding the end-of-line character. The
for expression iterates through these lines and prints for each the length of
the line, a space, and the line itself. If there were no arguments supplied on
the command line, the final else clause will print a message to the standard
error stream. If you place this code in a file named countchars1.scala,
and run it on itself with:
$ scala countchars1.scala countchars1.scala
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You should see:
22 import scala.io.Source
0
22 if (args.length > 0) {
0
51
for (line <- Source.fromFile(args(0)).getLines())
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37
println(line.length + " " + line)
1 }
4 else
46
Console.err.println("Please enter filename")
Although the script in its current form prints out the needed information, you
may wish to line up the numbers, right adjusted, and add a pipe character, so
that the output looks instead like:
22 | import scala.io.Source
0 |
22 | if (args.length > 0) {
0 |
51 |
for (line <- Source.fromFile(args(0)).getLines())
37 |
println(line.length + " " + line)
1 | }
4 | else
46 |
Console.err.println("Please enter filename")
To accomplish this, you can iterate through the lines twice. The first time
through you’ll determine the maximum width required by any line’s character count. The second time through you’ll print the output, using the maximum width calculated previously. Because you’ll be iterating through the
lines twice, you may as well assign them to a variable:
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val lines = Source.fromFile(args(0)).getLines().toList
The final toList is required because the getLines method returns an iterator. Once you’ve iterated through an iterator, it is spent. By transforming it
into a list via the toList call, you gain the ability to iterate as many times
as you wish, at the cost of storing all lines from the file in memory at once.
The lines variable, therefore, references a list of strings that contains the
contents of the file specified on the command line. Next, because you’ll be
calculating the width of each line’s character count twice, once per iteration,
you might factor that expression out into a small function, which calculates
the character width of the passed string’s length:
def widthOfLength(s: String) = s.length.toString.length
With this function, you could calculate the maximum width like this:
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var maxWidth = 0
for (line <- lines)
maxWidth = maxWidth.max(widthOfLength(line))
Here you iterate through each line with a for expression, calculate the character width of that line’s length, and, if it is larger than the current maximum,
assign it to maxWidth, a var that was initialized to 0. (The max method,
which you can invoke on any Int, returns the greater of the value on which
it was invoked and the value passed to it.) Alternatively, if you prefer to find
the maximum without vars, you could first find the longest line like this:
val longestLine = lines.reduceLeft(
(a, b) => if (a.length > b.length) a else b
)
The reduceLeft method applies the passed function to the first two elements
in lines, then applies it to the result of the first application and the next
element in lines, and so on, all the way through the list. On each such
application, the result will be the longest line encountered so far because the
passed function, (a, b) => if (a.length > b.length) a else b, returns
the longest of the two passed strings. “reduceLeft” will return the result
of the last application of the function, which in this case will be the longest
string element contained in lines.
Given this result, you can calculate the maximum width by passing the
longest line to widthOfLength:
val maxWidth = widthOfLength(longestLine)
All that remains is to print out the lines with proper formatting. You can
do that like this:
for (line <- lines) {
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val numSpaces = maxWidth - widthOfLength(line)
val padding = " " * numSpaces
println(padding + line.length + " | " + line)
}
In this for expression, you once again iterate through the lines. For each
line, you first calculate the number of spaces required before the line length
and assign it to numSpaces. Then you create a string containing numSpaces
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97
Conclusion
Chapter 3 · Next Steps in Scala
spaces with the expression " " * numSpaces. Finally, you print out the information with the desired formatting. The entire script looks as shown in
Listing 3.11:
import scala.io.Source
def widthOfLength(s: String) = s.length.toString.length
if (args.length > 0) {
val lines = Source.fromFile(args(0)).getLines().toList
val longestLine = lines.reduceLeft(
(a, b) => if (a.length > b.length) a else b
)
val maxWidth = widthOfLength(longestLine)
for (line <- lines) {
val numSpaces = maxWidth - widthOfLength(line)
val padding = " " * numSpaces
println(padding + line.length + " | " + line)
}
}
else
Console.err.println("Please enter filename")
Listing 3.11 · Printing formatted character counts for the lines of a file.
Conclusion
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With the knowledge you’ve gained in this chapter, you should be able to start
using Scala for small tasks, especially scripts. In later chapters, we will dive
further into these topics and introduce other topics that weren’t even hinted
at here.
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98
Chapter 4
Classes and Objects
You’ve now seen the basics of classes and objects in Scala from the previous
two chapters. In this chapter, we’ll take you a bit deeper. You’ll learn more
about classes, fields, and methods, and get an overview of semicolon inference. We’ll discuss singleton objects, including how to use them to write
and run a Scala application. If you are familiar with Java, you’ll find that the
concepts in Scala are similar, but not exactly the same. So even if you’re a
Java guru, it will pay to read on.
4.1
Classes, fields, and methods
A class is a blueprint for objects. Once you define a class, you can create
objects from the class blueprint with the keyword new. For example, given
the class definition:
class ChecksumAccumulator {
// class definition goes here
}
You can create ChecksumAccumulator objects with:
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new ChecksumAccumulator
Inside a class definition, you place fields and methods, which are collectively
called members. Fields, which you define with either val or var, are variables that refer to objects. Methods, which you define with def, contain
executable code. The fields hold the state, or data, of an object, whereas the
methods use that data to do the computational work of the object. When you
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Chapter 4 · Classes and Objects
100
instantiate a class, the runtime sets aside some memory to hold the image
of that object’s state (i.e., the content of its variables). For example, if you
defined a ChecksumAccumulator class and gave it a var field named sum:
class ChecksumAccumulator {
var sum = 0
}
and you instantiated it twice with:
val acc = new ChecksumAccumulator
val csa = new ChecksumAccumulator
The image of the objects in memory might look like this:
sum
acc
0
sum
csa
Since sum, a field declared inside class ChecksumAccumulator, is a var,
not a val, you can later reassign to sum a different Int value, like this:
acc.sum = 3
Now the picture would look like this:
sum
3
sum
0
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acc
csa
One thing to notice about this picture is that there are two sum variables,
one in the object referenced by acc and the other in the object referenced
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Section 4.1
Chapter 4 · Classes and Objects
by csa. Fields are also known as instance variables, because every instance
gets its own set of the variables. Collectively, an object’s instance variables
make up the memory image of the object. You see this illustrated here not
only in that you see two sum variables, but also that when you changed one,
the other was unaffected.
Another thing to note in this example is that you were able to mutate the
object acc referred to, even though acc is a val. What you can’t do with
acc (or csa), given that they are vals, not vars, is reassign a different object
to them. For example, the following attempt would fail:
// Won't compile, because acc is a val
acc = new ChecksumAccumulator
What you can count on, therefore, is that acc will always refer to the same
ChecksumAccumulator object with which you initialize it, but the fields
contained inside that object might change over time.
One important way to pursue robustness of an object is to ensure that the
object’s state—the values of its instance variables—remains valid during its
entire lifetime. The first step is to prevent outsiders from accessing the fields
directly by making the fields private. Because private fields can only be
accessed by methods defined in the same class, all the code that can update
the state will be localized to the class. To declare a field private, you place a
private access modifier in front of the field, like this:
class ChecksumAccumulator {
private var sum = 0
}
Given this definition of ChecksumAccumulator, any attempt to access sum
from the outside of the class would fail:
val acc = new ChecksumAccumulator
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acc.sum = 5 // Won't compile, because sum is private
Note
The way you make members public in Scala is by not explicitly specifying
any access modifier. Put another way, where you’d say “public” in Java,
you simply say nothing in Scala. Public is Scala’s default access level.
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Section 4.1
Chapter 4 · Classes and Objects
Now that sum is private, the only code that can access sum is code defined
inside the body of the class itself. Thus, ChecksumAccumulator won’t be of
much use to anyone unless we define some methods in it:
class ChecksumAccumulator {
private var sum = 0
def add(b: Byte): Unit = {
sum += b
}
def checksum(): Int = {
return ~(sum & 0xFF) + 1
}
}
The ChecksumAccumulator now has two methods, add and checksum, both
of which exhibit the basic form of a function definition, shown in Figure 2.1
on page 69.
Any parameters to a method can be used inside the method. One important characteristic of method parameters in Scala is that they are vals,
not vars.1 If you attempt to reassign a parameter inside a method in Scala,
therefore, it won’t compile:
def add(b: Byte): Unit = {
b = 1
// This won't compile, because b is a val
sum += b
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}
Although add and checksum in this version of ChecksumAccumulator
correctly implement the desired functionality, you can express them using a
more concise style. First, the return at the end of the checksum method
is superfluous and can be dropped. In the absence of any explicit return
statement, a Scala method returns the last value computed by the method.
The recommended style for methods is in fact to avoid having explicit,
and especially multiple, return statements. Instead, think of each method
as an expression that yields one value, which is returned. This philosophy
will encourage you to make methods quite small, to factor larger methods
1 The reason parameters are vals is that vals are easier to reason about. You needn’t
look further to determine if a val is reassigned, as you must do with a var.
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102
Section 4.1
Chapter 4 · Classes and Objects
into multiple smaller ones. On the other hand, design choices depend on the
design context, and Scala makes it easy to write methods that have multiple,
explicit returns if that’s what you desire.
Because all checksum does is calculate a value, it does not need an explicit return. Another shorthand for methods is that you can leave off the
curly braces if a method computes only a single result expression. If the
result expression is short, it can even be placed on the same line as the def
itself. For the utmost in conciseness, you can leave off the result type and
Scala will infer it. With these changes, class ChecksumAccumulator looks
like this:
class ChecksumAccumulator {
private var sum = 0
def add(b: Byte) = sum += b
def checksum() = ~(sum & 0xFF) + 1
}
Although the Scala compiler will correctly infer the result types of the
add and checksum methods shown in the previous example, readers of the
code will also need to mentally infer the result types by studying the bodies
of the methods. As a result it is often better to explicitly provide the result
types of public methods declared in a class even when the compiler would
infer it for you. Listing 4.1 shows this style.
// In file ChecksumAccumulator.scala
class ChecksumAccumulator {
private var sum = 0
def add(b: Byte): Unit = { sum += b }
def checksum(): Int = ~(sum & 0xFF) + 1
}
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Listing 4.1 · Final version of class ChecksumAccumulator.
Methods with a result type of Unit, such as ChecksumAccumulator’s
add method, are executed for their side effects. A side effect is generally
defined as mutating state somewhere external to the method or performing
an I/O action. In add’s case, the side effect is that sum is reassigned. A
method that is executed only for its side effects is known as a procedure.
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103
Section 4.2
4.2
Chapter 4 · Classes and Objects
Semicolon inference
In a Scala program, a semicolon at the end of a statement is usually optional.
You can type one if you want but you don’t have to if the statement appears
by itself on a single line. On the other hand, a semicolon is required if you
write multiple statements on a single line:
val s = "hello"; println(s)
If you want to enter a statement that spans multiple lines, most of the time
you can simply enter it and Scala will separate the statements in the correct
place. For example, the following is treated as one four-line statement:
if (x < 2)
println("too small")
else
println("ok")
Occasionally, however, Scala will split a statement into two parts against
your wishes:
x
+ y
This parses as two statements x and +y. If you intend it to parse as one
statement x + y, you can always wrap it in parentheses:
(x
+ y)
Alternatively, you can put the + at the end of a line. For just this reason,
whenever you are chaining an infix operation such as +, it is a common Scala
style to put the operators at the end of the line instead of the beginning:
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x +
y +
z
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Section 4.3
Chapter 4 · Classes and Objects
The rules of semicolon inference
The precise rules for statement separation are surprisingly simple for
how well they work. In short, a line ending is treated as a semicolon
unless one of the following conditions is true:
1. The line in question ends in a word that would not be legal as the
end of a statement, such as a period or an infix operator.
2. The next line begins with a word that cannot start a statement.
3. The line ends while inside parentheses (...) or brackets [...],
because these cannot contain multiple statements anyway.
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4.3
Singleton objects
As mentioned in Chapter 1, one way in which Scala is more object-oriented
than Java is that classes in Scala cannot have static members. Instead, Scala
has singleton objects. A singleton object definition looks like a class definition, except instead of the keyword class you use the keyword object.
Listing 4.2 shows an example.
The singleton object in this figure is named ChecksumAccumulator, the
same name as the class in the previous example. When a singleton object
shares the same name with a class, it is called that class’s companion object.
You must define both the class and its companion object in the same source
file. The class is called the companion class of the singleton object. A class
and its companion object can access each other’s private members.
The ChecksumAccumulator singleton object has one method, named
calculate, which takes a String and calculates a checksum for the characters in the String. It also has one private field, cache, a mutable map
in which previously calculated checksums are cached.2 The first line of the
method, “if (cache.contains(s))”, checks the cache to see if the passed
string is already contained as a key in the map. If so, it just returns the
mapped value, cache(s). Otherwise, it executes the else clause, which cal2 We
used a cache here to show a singleton object with a field. A cache such as this is
a performance optimization that trades off memory for computation time. In general, you
would likely use such a cache only if you encountered a performance problem that the cache
solves, and might use a weak map, such as WeakHashMap in scala.collection.jcl, so
that entries in the cache could be garbage collected if memory becomes scarce.
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// In file ChecksumAccumulator.scala
import scala.collection.mutable
object ChecksumAccumulator {
private val cache = mutable.Map.empty[String, Int]
def calculate(s: String): Int =
if (cache.contains(s))
cache(s)
else {
val acc = new ChecksumAccumulator
for (c <- s)
acc.add(c.toByte)
val cs = acc.checksum()
cache += (s -> cs)
cs
}
}
Listing 4.2 · Companion object for class ChecksumAccumulator.
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culates the checksum. The first line of the else clause defines a val named
acc and initializes it with a new ChecksumAccumulator instance.3 The next
line is a for expression, which cycles through each character in the passed
string, converts the character to a Byte by invoking toByte on it, and passes
that to the add method of the ChecksumAccumulator instances to which
acc refers. After the for expression completes, the next line of the method
invokes checksum on acc, which gets the checksum for the passed String,
and stores it into a val named cs. In the next line, cache += (s -> cs),
the passed string key is mapped to the integer checksum value, and this keyvalue pair is added to the cache map. The last expression of the method, cs,
ensures the checksum is the result of the method.
If you are a Java programmer, one way to think of singleton objects is
as the home for any static methods you might have written in Java. You can
invoke methods on singleton objects using a similar syntax: the name of the
singleton object, a dot, and the name of the method. For example, you can
3 Because the keyword new is only used to instantiate classes, the new object created here
is an instance of the ChecksumAccumulator class, not the singleton object of the same name.
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107
invoke the calculate method of singleton object ChecksumAccumulator
like this:
ChecksumAccumulator.calculate("Every value is an object.")
A singleton object is more than a holder of static methods, however. It is a
first-class object. You can think of a singleton object’s name, therefore, as a
“name tag” attached to the object:
cache
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ChecksumAccumulator
mutable
map
Defining a singleton object doesn’t define a type (at the Scala level of
abstraction). Given just a definition of object ChecksumAccumulator, you
can’t make a variable of type ChecksumAccumulator. Rather, the type
named ChecksumAccumulator is defined by the singleton object’s companion class. However, singleton objects extend a superclass and can mix
in traits. Given each singleton object is an instance of its superclasses and
mixed-in traits, you can invoke its methods via these types, refer to it from
variables of these types, and pass it to methods expecting these types. We’ll
show some examples of singleton objects inheriting from classes and traits
in Chapter 13.
One difference between classes and singleton objects is that singleton
objects cannot take parameters, whereas classes can. Because you can’t instantiate a singleton object with the new keyword, you have no way to pass
parameters to it. Each singleton object is implemented as an instance of
a synthetic class referenced from a static variable, so they have the same
initialization semantics as Java statics.4 In particular, a singleton object is
initialized the first time some code accesses it.
A singleton object that does not share the same name with a companion
class is called a standalone object. You can use standalone objects for many
purposes, including collecting related utility methods together or defining an
entry point to a Scala application. This use case is shown in the next section.
4 The name of the synthetic class is the object name plus a dollar sign.
Thus the synthetic
class for the singleton object named ChecksumAccumulator is ChecksumAccumulator$.
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Section 4.4
4.4
Chapter 4 · Classes and Objects
A Scala application
To run a Scala program, you must supply the name of a standalone singleton
object with a main method that takes one parameter, an Array[String],
and has a result type of Unit. Any standalone object with a main method of
the proper signature can be used as the entry point into an application. An
example is shown in Listing 4.3:
// In file Summer.scala
import ChecksumAccumulator.calculate
object Summer {
def main(args: Array[String]) = {
for (arg <- args)
println(arg + ": " + calculate(arg))
}
}
Listing 4.3 · The Summer application.
The name of the singleton object in Listing 4.3 is Summer. Its main
method has the proper signature, so you can use it as an application. The
first statement in the file is an import of the calculate method defined in the
ChecksumAccumulator object in the previous example. This import statement allows you to use the method’s simple name in the rest of the file.5 The
body of the main method simply prints out each argument and the checksum
for the argument, separated by a colon.
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Note
Scala implicitly imports members of packages java.lang and scala, as
well as the members of a singleton object named Predef, into every Scala
source file. Predef, which resides in package scala, contains many
useful methods. For example, when you say println in a Scala source
file, you’re actually invoking println on Predef. (Predef.println
turns around and invokes Console.println, which does the real work.)
When you say assert, you’re invoking Predef.assert.
5 If
you’re a Java programmer, you can think of this import as similar to the static import feature introduced in Java 5. One difference in Scala, however, is that you can import
members from any object, not just singleton objects.
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To run the Summer application, place the code from Listing 4.3 into a
file named Summer.scala. Because Summer uses ChecksumAccumulator,
place the code for ChecksumAccumulator, both the class shown in Listing 4.1 and its companion object shown in Listing 4.2, into a file named
ChecksumAccumulator.scala.
One difference between Scala and Java is that whereas Java requires you
to put a public class in a file named after the class—for example, you’d
put class SpeedRacer in file SpeedRacer.java—in Scala, you can name
.scala files anything you want, no matter what Scala classes or code you
put in them. In general in the case of non-scripts, however, it is recommended
style to name files after the classes they contain as is done in Java, so that
programmers can more easily locate classes by looking at file names. This is
the approach we’ve taken with the two files in this example, Summer.scala
and ChecksumAccumulator.scala.
Neither ChecksumAccumulator.scala nor Summer.scala are scripts,
because they end in a definition. A script, by contrast, must end in a result expression. Thus if you try to run Summer.scala as a script, the Scala
interpreter will complain that Summer.scala does not end in a result expression (assuming of course you didn’t add any expression of your own after
the Summer object definition). Instead, you’ll need to actually compile these
files with the Scala compiler, then run the resulting class files. One way to
do this is to use scalac, which is the basic Scala compiler, like this:
$ scalac ChecksumAccumulator.scala Summer.scala
This compiles your source files, but there may be a perceptible delay before
the compilation finishes. The reason is that every time the compiler starts up,
it spends time scanning the contents of jar files and doing other initial work
before it even looks at the fresh source files you submit to it. For this reason,
the Scala distribution also includes a Scala compiler daemon called fsc (for
fast Scala compiler). You use it like this:
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$ fsc ChecksumAccumulator.scala Summer.scala
The first time you run fsc, it will create a local server daemon attached to
a port on your computer. It will then send the list of files to compile to the
daemon via the port, and the daemon will compile the files. The next time
you run fsc, the daemon will already be running, so fsc will simply send
the file list to the daemon, which will immediately compile the files. Using
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fsc, you only need to wait for the Java runtime to startup the first time. If
you ever want to stop the fsc daemon, you can do so with fsc -shutdown.
Running either of these scalac or fsc commands will produce Java
class files that you can then run via the scala command, the same command
you used to invoke the interpreter in previous examples. However, instead
of giving it a filename with a .scala extension containing Scala code to
interpret as you did in every previous example,6 in this case you’ll give it
the name of a standalone object containing a main method of the proper
signature. You can run the Summer application, therefore, by typing:
$ scala Summer of love
You will see checksums printed for the two command line arguments:
of: -213
love: -182
4.5
The App trait
Scala provides a trait, scala.App, that can save you some finger typing. Although we haven’t yet covered everything you’ll need to understand exactly
how this trait works, we figured you’d want to know about it now anyway.
Listing 4.4 shows an example:
import ChecksumAccumulator.calculate
object FallWinterSpringSummer extends App {
for (season <- List("fall", "winter", "spring"))
println(season + ": " + calculate(season))
}
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Listing 4.4 · Using the App trait.
To use the trait, you first write “extends App” after the name of your
singleton object. Then instead of writing a main method, you place the code
6 The actual mechanism that the scala program uses to “interpret” a Scala source file is
that it compiles the Scala source code to Java bytecodes, loads them immediately via a class
loader, and executes them.
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you would have put in the main method directly between the curly braces of
the singleton object. You can access command-line arguments via an array
of strings named args. That’s it. You can compile and run this application
just like any other.
4.6
Conclusion
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This chapter has given you the basics of classes and objects in Scala, and
shown you how to compile and run applications. In the next chapter, you’ll
learn about Scala’s basic types and how to use them.
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Chapter 5
Basic Types and Operations
Now that you’ve seen classes and objects in action, it’s a good time to look
at Scala’s basic types and operations in more depth. If you’re familiar with
Java, you’ll be glad to find that Java’s basic types and operators have the
same meaning in Scala. However, there are some interesting differences
that will make this chapter worth reading even if you’re an experienced Java
developer. Because some aspects of Scala covered in this chapter are essentially the same as in Java, we’ve inserted notes indicating what sections Java
developers can safely skip.
In this chapter, you’ll get an overview of Scala’s basic types, including
Strings and the value types Int, Long, Short, Byte, Float, Double, Char,
and Boolean. You’ll learn the operations you can perform on these types,
including how operator precedence works in Scala expressions. You’ll also
learn how implicit conversions can “enrich” variants of these basic types,
giving you additional operations beyond those supported by Java.
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5.1
Some basic types
Several fundamental types of Scala, along with the ranges of values instances
of these types may have, are shown in Table 5.1. Collectively, types Byte,
Short, Int, Long, and Char are called integral types. The integral types plus
Float and Double are called numeric types.
Other than String, which resides in package java.lang, all of the types
shown in Table 5.1 are members of package scala.1 For example, the full
1 Packages, which were briefly described in Step 1 in Chapter 2, will be covered in depth
in Chapter 13.
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Table 5.1 · Some basic types
Basic type
Range
Byte
Short
Int
Long
Char
String
Float
Double
Boolean
8-bit signed two’s complement integer (-27 to 27 - 1, inclusive)
16-bit signed two’s complement integer (-215 to 215 - 1, inclusive)
32-bit signed two’s complement integer (-231 to 231 - 1, inclusive)
64-bit signed two’s complement integer (-263 to 263 - 1, inclusive)
16-bit unsigned Unicode character (0 to 216 - 1, inclusive)
a sequence of Chars
32-bit IEEE 754 single-precision float
64-bit IEEE 754 double-precision float
true or false
name of Int is scala.Int. However, given that all the members of package
scala and java.lang are automatically imported into every Scala source
file, you can just use the simple names (i.e., names like Boolean, Char, or
String) everywhere.
Savvy Java developers will note that Scala’s basic types have the exact
same ranges as the corresponding types in Java. This enables the Scala compiler to transform instances of Scala value types, such as Int or Double,
down to Java primitive types in the bytecodes it produces.
5.2
Literals
All of the basic types listed in Table 5.1 can be written with literals. A literal
is a way to write a constant value directly in code.
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Fast track for Java programmers
The syntax of most literals shown in this section are exactly the same as in
Java, so if you’re a Java master, you can safely skip much of this section.
Some differences you should read about are Scala’s literals for raw strings
and symbols, described starting on page 117, and string interpolation,
described starting on page 119. Also, Scala does not support octal literals;
integer literals that start with a 0, such as 031, do not compile.
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Integer literals
Integer literals for the types Int, Long, Short, and Byte come in two forms:
decimal and hexadecimal. The way an integer literal begins indicates the
base of the number. If the number begins with a 0x or 0X, it is hexadecimal
(base 16), and may contain 0 through 9 as well as upper or lowercase digits
A through F. Some examples are:
scala> val hex = 0x5
hex: Int = 5
scala> val hex2 = 0x00FF
hex2: Int = 255
scala> val magic = 0xcafebabe
magic: Int = -889275714
Note that the Scala shell always prints integer values in base 10, no matter what literal form you may have used to initialize it. Thus the interpreter
displays the value of the hex2 variable you initialized with literal 0x00FF as
decimal 255. (Of course, you don’t need to take our word for it. A good way
to start getting a feel for the language is to try these statements out in the
interpreter as you read this chapter.) If the number begins with a non-zero
digit, and is otherwise undecorated, it is decimal (base 10). For example:
scala> val dec1 = 31
dec1: Int = 31
scala> val dec2 = 255
dec2: Int = 255
scala> val dec3 = 20
dec3: Int = 20
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If an integer literal ends in an L or l, it is a Long; otherwise it is an Int.
Some examples of Long integer literals are:
scala> val prog = 0XCAFEBABEL
prog: Long = 3405691582
scala> val tower = 35L
tower: Long = 35
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scala> val of = 31l
of: Long = 31
If an Int literal is assigned to a variable of type Short or Byte, the
literal is treated as if it were a Short or Byte type so long as the literal value
is within the valid range for that type. For example:
scala> val little: Short = 367
little: Short = 367
scala> val littler: Byte = 38
littler: Byte = 38
Floating point literals
Floating point literals are made up of decimal digits, optionally containing a
decimal point, and optionally followed by an E or e and an exponent. Some
examples of floating-point literals are:
scala> val big = 1.2345
big: Double = 1.2345
scala> val bigger = 1.2345e1
bigger: Double = 12.345
scala> val biggerStill = 123E45
biggerStill: Double = 1.23E47
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Note that the exponent portion means the power of 10 by which the other
portion is multiplied. Thus, 1.2345e1 is 1.2345 times 101 , which is 12.345.
If a floating-point literal ends in an F or f, it is a Float; otherwise it is a
Double. Optionally, a Double floating-point literal can end in D or d. Some
examples of Float literals are:
scala> val little = 1.2345F
little: Float = 1.2345
scala> val littleBigger = 3e5f
littleBigger: Float = 300000.0
That last value expressed as a Double could take these (and other) forms:
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scala> val anotherDouble = 3e5
anotherDouble: Double = 300000.0
scala> val yetAnother = 3e5D
yetAnother: Double = 300000.0
Character literals
Character literals are composed of any Unicode character between single
quotes, such as:
scala> val a = 'A'
a: Char = A
In addition to providing an explicit character between the single quotes, you
can identify a character using its Unicode code point. To do so, write \u
followed by four hex digits with the code point, like this:
scala> val d = '\u0041'
d: Char = A
scala> val f = '\u0044'
f: Char = D
In fact, such Unicode characters can appear anywhere in a Scala program.
For instance you could also write an identifier like this:
scala> val B\u0041\u0044 = 1
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BAD: Int = 1
This identifier is treated as identical to BAD, the result of expanding the two
Unicode characters in the code above. In general, it is a bad idea to name
identifiers like this because it is hard to read. Rather, this syntax is intended
to allow Scala source files that include non-ASCII Unicode characters to be
represented in ASCII.
Finally, there are also a few character literals represented by special escape sequences, shown in Table 5.2. For example:
scala> val backslash = '\\'
backslash: Char = \
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Table 5.2 · Special character literal escape sequences
Literal
\n
\b
\t
\f
\r
\"
\'
\\
Meaning
line feed (\u000A)
backspace (\u0008)
tab (\u0009)
form feed (\u000C)
carriage return (\u000D)
double quote (\u0022)
single quote (\u0027)
backslash (\u005C)
String literals
A string literal is composed of characters surrounded by double quotes:
scala> val hello = "hello"
hello: String = hello
The syntax of the characters within the quotes is the same as with character
literals. For example:
scala> val escapes = "\\\"\'"
escapes: String = \"'
Because this syntax is awkward for strings that contain a lot of escape
sequences or strings that span multiple lines, Scala includes a special syntax
for raw strings. You start and end a raw string with three double quotation
marks in a row ("""). The interior of a raw string may contain any characters
whatsoever, including newlines, quotation marks, and special characters, except of course three quotes in a row. For example, the following program
prints out a message using a raw string:
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println("""Welcome to Ultamix 3000.
Type "HELP" for help.""")
However, running this code does not produce quite what is desired:
Welcome to Ultamix 3000.
Type "HELP" for help.
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The issue is that the leading spaces before the second line are included in the
string! To help with this common situation, you can call stripMargin on
strings. To use this method, put a pipe character (|) at the front of each line,
and then call stripMargin on the whole string:
println("""|Welcome to Ultamix 3000.
|Type "HELP" for help.""".stripMargin)
Now the code behaves as desired:
Welcome to Ultamix 3000.
Type "HELP" for help.
Symbol literals
A symbol literal is written 'ident, where ident can be any alphanumeric
identifier. Such literals are mapped to instances of the predefined class
scala.Symbol. Specifically, the literal 'cymbal will be expanded by the
compiler to a factory method invocation: Symbol("cymbal"). Symbol literals are typically used in situations where you would use just an identifier
in a dynamically typed language. For instance, you might want to define a
method that updates a record in a database:
scala> def updateRecordByName(r: Symbol, value: Any) = {
// code goes here
}
updateRecordByName: (Symbol,Any)Unit
The method takes as parameters a symbol indicating the name of a record
field and a value with which the field should be updated in the record. In
a dynamically typed language, you could invoke this operation passing an
undeclared field identifier to the method, but in Scala this would not compile:
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scala> updateRecordByName(favoriteAlbum, "OK Computer")
:6: error: not found: value favoriteAlbum
updateRecordByName(favoriteAlbum, "OK Computer")
ˆ
Instead, and almost as concisely, you can pass a symbol literal:
scala> updateRecordByName('favoriteAlbum, "OK Computer")
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There is not much you can do with a symbol, except find out its name:
scala> val s = 'aSymbol
s: Symbol = 'aSymbol
scala> val nm = s.name
nm: String = aSymbol
Another thing that’s noteworthy is that symbols are interned. If you write
the same symbol literal twice, both expressions will refer to the exact same
Symbol object.
Boolean literals
The Boolean type has two literals, true and false:
scala> val bool = true
bool: Boolean = true
scala> val fool = false
fool: Boolean = false
That’s all there is to it. You are now literally2 an expert in Scala.
5.3
String interpolation
Scala includes a flexible mechanism for string interpolation, which allows
you to embed expressions within string literals. Its most common use case is
to provide a concise and readable alternative to string concatenation. Here’s
an example:
val name = "reader"
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println(s"Hello, $name!")
The expression, s"Hello, $name!" is a processed string literal. Because the letter s immediately precedes the open quote, Scala will use the s
string interpolator to process the literal. The s interpolator will evaluate each
embedded expression, invoke toString on each result, and replace the embedded expressions in the literal with those results. Thus s"Hello, $name!"
yields "Hello, reader!", the same result as "Hello, " + name + "!".
2 figuratively
speaking
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You can place any expression after a dollar sign ($) in a processed string
literal. For single-variable expressions, you can often just place the variable
name after the dollar sign. Scala will interpret all characters up to the first
non-identifier character as the expression. If the expression includes nonidentifier characters, you must place it in curly braces, with the open curly
brace immediately following the dollar sign. Here’s an example:
scala> s"The answer is ${6 * 7}."
res0: String = The answer is 42.
Scala provides two other string interpolators by default: raw and f. The
raw string interpolator behaves like s, except it does not recognize character
literal escape sequences (such as those shown in Table 5.2). For example,
the following statement prints four backslashes, not two:
println(raw"No\\\\escape!") // prints: No\\\\escape!
The f string interpolator allows you to attach printf-style formatting
instructions to embedded expressions. You place the instructions after the
expression, starting with a percent sign (%), using the syntax specified by
java.util.Formatter. For example, here’s how you might format pi:
scala> f"${math.Pi}%.5f"
res1: String = 3.14159
If you provide no formatting instructions for an embedded expression,
the f string interpolator will default to %s, which means the toString value
will be substituted, just like the s string interpolator. For example:
scala> val pi = "Pi"
pi: String = Pi
scala> f"$pi is approximately ${math.Pi}%.8f."
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res2: String = Pi is approximately 3.14159265.
In Scala, string interpolation is implemented by rewriting code at compile time. The compiler will treat any expression consisting of an identifier
followed immediately by the open double quote of a string literal as a string
interpolator expression. The s, f, and raw string interpolators are implemented via this general mechanism. Libraries and users can define other
string interpolators for other purposes.
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121
Operators are methods
Scala provides a rich set of operators for its basic types. As mentioned in
previous chapters, these operators are actually just a nice syntax for ordinary
method calls. For example, 1 + 2 really means the same thing as 1.+(2).
In other words, class Int contains a method named + that takes an Int and
returns an Int result. This + method is invoked when you add two Ints:
scala> val sum = 1 + 2
// Scala invokes 1.+(2)
sum: Int = 3
To prove this to yourself, you can write the expression explicitly as a
method invocation:
scala> val sumMore = 1.+(2)
sumMore: Int = 3
In fact, Int contains several overloaded + methods that take different
parameter types.3 For example, Int has another method, also named +, that
takes and returns a Long. If you add a Long to an Int, this alternate + method
will be invoked, as in:
scala> val longSum = 1 + 2L
// Scala invokes 1.+(2L)
longSum: Long = 3
The + symbol is an operator—an infix operator to be specific. Operator
notation is not limited to methods like + that look like operators in other
languages. You can use any method in operator notation. For example,
class String has a method, indexOf, that takes one Char parameter. The
indexOf method searches the string for the first occurrence of the specified
character and returns its index or -1 if it doesn’t find the character. You can
use indexOf as an operator, like this:
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scala> val s = "Hello, world!"
s: String = Hello, world!
scala> s indexOf 'o'
// Scala invokes s.indexOf('o')
res0: Int = 4
3 Overloaded methods have the same name but different argument types.
More on method
overloading in Section 6.11.
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In addition, String offers an overloaded indexOf method that takes
two parameters, the character for which to search and an index at which
to start. (The other indexOf method, shown previously, starts at index zero,
the beginning of the String.) Even though this indexOf method takes two
arguments, you can use it in operator notation. But whenever you call a
method that takes multiple arguments using operator notation, you have to
place those arguments in parentheses. For example, here’s how you use this
other indexOf form as an operator (continuing from the previous example):
scala> s indexOf ('o', 5) // Scala invokes s.indexOf('o', 5)
res1: Int = 8
Any method can be an operator
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In Scala operators are not special language syntax; any method can
be an operator. What makes a method an operator is how you use it.
When you write “s.indexOf('o')”, indexOf is not an operator. But
when you write “s indexOf 'o'”, indexOf is an operator, because
you’re using it in operator notation.
So far, you’ve seen examples of infix operator notation, which means the
method to invoke sits between the object and the parameter (or parameters)
you wish to pass to the method, as in “7 + 2”. Scala also has two other
operator notations: prefix and postfix. In prefix notation, you put the method
name before the object on which you are invoking the method (for example,
the ‘-’ in -7). In postfix notation, you put the method after the object (for
example, the “toLong” in “7 toLong”).
In contrast to the infix operator notation—in which operators take two
operands, one to the left and the other to the right—prefix and postfix operators are unary: they take just one operand. In prefix notation, the operand
is to the right of the operator. Some examples of prefix operators are -2.0,
!found, and ~0xFF. As with the infix operators, these prefix operators are
a shorthand way of invoking methods. In this case, however, the name
of the method has “unary_” prepended to the operator character. For instance, Scala will transform the expression -2.0 into the method invocation “(2.0).unary_-”. You can demonstrate this to yourself by typing the
method call both via operator notation and explicitly:
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scala> -2.0
// Scala invokes (2.0).unary_-
res2: Double = -2.0
scala> (2.0).unary_res3: Double = -2.0
The only identifiers that can be used as prefix operators are +, -, !, and ~.
Thus, if you define a method named unary_!, you could invoke that method
on a value or variable of the appropriate type using prefix operator notation,
such as !p. But if you define a method named unary_*, you wouldn’t be
able to use prefix operator notation because * isn’t one of the four identifiers
that can be used as prefix operators. You could invoke the method normally,
as in p.unary_*, but if you attempted to invoke it via *p, Scala will parse it
as if you’d written *.p, which is probably not what you had in mind!4
Postfix operators are methods that take no arguments, when they are invoked without a dot or parentheses. In Scala, you can leave off empty parentheses on method calls. The convention is that you include parentheses if the
method has side effects, such as println(), but you can leave them off if
the method has no side effects, such as toLowerCase invoked on a String:
scala> val s = "Hello, world!"
s: String = Hello, world!
scala> s.toLowerCase
res4: String = hello, world!
In this latter case of a method that requires no arguments, you can alternatively leave off the dot and use postfix operator notation:
scala> s toLowerCase
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res5: String = hello, world!
In this case, toLowerCase is used as a postfix operator on the operand s.
Therefore, to see what operators you can use with Scala’s basic types,
all you really need to do is look at the methods declared in the type’s classes
in the Scala API documentation. Given that this is a Scala tutorial, however,
we’ll give you a quick tour of most of these methods in the next few sections.
4 All is not necessarily lost, however. There is an extremely slight chance your program
with the *p might compile as C++.
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Fast track for Java programmers
Many aspects of Scala described in the remainder of this chapter are the
same as in Java. If you’re a Java guru in a rush, you can safely skip to
Section 5.8 on page 128, which describes how Scala differs from Java in
the area of object equality.
5.5
Arithmetic operations
You can invoke arithmetic methods via infix operator notation for addition
(+), subtraction (-), multiplication (*), division (/), and remainder (%) on any
numeric type. Here are some examples:
scala> 1.2 + 2.3
res6: Double = 3.5
scala> 3 - 1
res7: Int = 2
scala> 'b' - 'a'
res8: Int = 1
scala> 2L * 3L
res9: Long = 6
scala> 11 / 4
res10: Int = 2
scala> 11 % 4
res11: Int = 3
scala> 11.0f / 4.0f
res12: Float = 2.75
scala> 11.0 % 4.0
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res13: Double = 3.0
When both the left and right operands are integral types (Int, Long,
Byte, Short, or Char), the / operator will tell you the whole number portion of the quotient, excluding any remainder. The % operator indicates the
remainder of an implied integer division.
The floating-point remainder you get with % is not the one defined by the
IEEE 754 standard. The IEEE 754 remainder uses rounding division, not
truncating division, in calculating the remainder, so it is quite different from
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the integer remainder operation. If you really want an IEEE 754 remainder,
you can call IEEEremainder on scala.math, as in:
scala> math.IEEEremainder(11.0, 4.0)
res14: Double = -1.0
The numeric types also offer unary prefix operators + (method unary_+)
and - (method unary_-), which allow you to indicate whether a literal number is positive or negative, as in -3 or +4.0. If you don’t specify a unary
+ or -, a literal number is interpreted as positive. Unary + exists solely for
symmetry with unary -, but has no effect. The unary - can also be used to
negate a variable. Here are some examples:
scala> val neg = 1 + -3
neg: Int = -2
scala> val y = +3
y: Int = 3
scala> -neg
res15: Int = 2
5.6
Relational and logical operations
You can compare numeric types with relational methods greater than (>), less
than (<), greater than or equal to (>=), and less than or equal to (<=), which
yield a Boolean result. In addition, you can use the unary ‘!’ operator (the
unary_! method) to invert a Boolean value. Here are a few examples:
scala> 1 > 2
res16: Boolean = false
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scala> 1 < 2
res17: Boolean = true
scala> 1.0 <= 1.0
res18: Boolean = true
scala> 3.5f >= 3.6f
res19: Boolean = false
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scala> 'a' >= 'A'
res20: Boolean = true
scala> val untrue = !true
untrue: Boolean = false
Logical methods, logical-and (&& and &) and logical-or (|| and |), take
Boolean operands in infix notation and yield a Boolean result. For example:
scala> val toBe = true
toBe: Boolean = true
scala> val question = toBe || !toBe
question: Boolean = true
scala> val paradox = toBe && !toBe
paradox: Boolean = false
The && and || operations short-circuit as in Java: expressions built from
these operators are only evaluated as far as needed to determine the result.
In other words, the right-hand side of && and || expressions won’t be evaluated if the left-hand side determines the result. For example, if the left-hand
side of a && expression evaluates to false, the result of the expression will
definitely be false, so the right-hand side is not evaluated. Likewise, if the
left-hand side of a || expression evaluates to true, the result of the expression will definitely be true, so the right-hand side is not evaluated.
scala> def salt() = { println("salt"); false }
salt: ()Boolean
scala> def pepper() = { println("pepper"); true }
pepper: ()Boolean
scala> pepper() && salt()
pepper
salt
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res21: Boolean = false
scala> salt() && pepper()
salt
res22: Boolean = false
In the first expression, pepper and salt are invoked, but in the second, only
salt is invoked. Given salt returns false, there’s no need to call pepper.
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If you want to evaluate the right hand side no matter what, use & and |
instead. The & method performs a logical-and operation, and | a logical-or,
but don’t short-ciruit like && and ||. Here’s an example:
scala> salt() & pepper()
salt
pepper
res23: Boolean = false
Note
You may be wondering how short-circuiting can work given operators are
just methods. Normally, all arguments are evaluated before entering a
method, so how can a method avoid evaluating its second argument? The
answer is that all Scala methods have a facility for delaying the evaluation
of their arguments, or even declining to evaluate them at all. The facility is
called by-name parameters and is discussed in Section 9.5.
5.7
Bitwise operations
Scala enables you to perform operations on individual bits of integer types
with several bitwise methods. The bitwise methods are: bitwise-and (&),
bitwise-or (|), and bitwise-xor (ˆ).5 The unary bitwise complement operator
(~, the method unary_~) inverts each bit in its operand. For example:
scala> 1 & 2
res24: Int = 0
scala> 1 | 2
res25: Int = 3
scala> 1 ˆ 3
res26: Int = 2
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scala> ~1
res27: Int = -2
The first expression, 1 & 2, bitwise-ands each bit in 1 (0001) and 2 (0010),
which yields 0 (0000). The second expression, 1 | 2, bitwise-ors each bit in
5 The
bitwise-xor method performs an exclusive or on its operands. Identical bits yield a
0. Different bits yield a 1. Thus 0011 ˆ 0101 yields 0110.
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the same operands, yielding 3 (0011). The third expression, 1 ˆ 3, bitwisexors each bit in 1 (0001) and 3 (0011), yielding 2 (0010). The final expression, ~1, inverts each bit in 1 (0001), yielding -2, which in binary looks like
11111111111111111111111111111110.
Scala integer types also offer three shift methods: shift left (<<), shift
right (>>), and unsigned shift right (>>>). The shift methods, when used in
infix operator notation, shift the integer value on the left of the operator by
the amount specified by the integer value on the right. Shift left and unsigned
shift right fill with zeroes as they shift. Shift right fills with the highest bit
(the sign bit) of the left-hand value as it shifts. Here are some examples:
scala> -1 >> 31
res28: Int = -1
scala> -1 >>> 31
res29: Int = 1
scala> 1 << 2
res30: Int = 4
-1 in binary is 11111111111111111111111111111111. In the first example, -1 >> 31, -1 is shifted to the right 31 bit positions. Since an Int
consists of 32 bits, this operation effectively moves the leftmost bit over until it becomes the rightmost bit.6 Since the >> method fills with ones as it
shifts right, because the leftmost bit of -1 is 1, the result is identical to the
original left operand, 32 one bits, or -1. In the second example, -1 >>> 31,
the leftmost bit is again shifted right until it is in the rightmost position, but
this time filling with zeroes along the way. Thus the result this time is binary
00000000000000000000000000000001, or 1. In the final example, 1 << 2,
the left operand, 1, is shifted left two positions (filling in with zeroes), resulting in binary 00000000000000000000000000000100, or 4.
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5.8
Object equality
If you want to compare two objects for equality, you can use either == or its
inverse !=. Here are a few simple examples:
6 The leftmost bit in an integer type is the sign bit. If the leftmost bit is 1, the number is
negative. If 0, the number is positive.
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scala> 1 == 2
res31: Boolean = false
scala> 1 != 2
res32: Boolean = true
scala> 2 == 2
res33: Boolean = true
These operations actually apply to all objects, not just basic types. For example, you can use == to compare lists:
scala> List(1, 2, 3) == List(1, 2, 3)
res34: Boolean = true
scala> List(1, 2, 3) == List(4, 5, 6)
res35: Boolean = false
Going further, you can compare two objects that have different types:
scala> 1 == 1.0
res36: Boolean = true
scala> List(1, 2, 3) == "hello"
res37: Boolean = false
You can even compare against null, or against things that might be null.
No exception will be thrown:
scala> List(1, 2, 3) == null
res38: Boolean = false
scala> null == List(1, 2, 3)
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res39: Boolean = false
As you see, == has been carefully crafted so that you get just the equality
comparison you want in most cases. This is accomplished with a very simple
rule: First check the left side for null. If it is not null, call the equals
method. Since equals is a method, the precise comparison you get depends
on the type of the left-hand argument. Since there is an automatic null check,
you do not have to do the check yourself.7
7 The automatic check does not look at the right-hand side, but any reasonable equals
method should return false if its argument is null.
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This kind of comparison will yield true on different objects, so long as
their contents are the same and their equals method is written to be based on
contents. For example, here is a comparison between two strings that happen
to have the same five letters in them:
scala> ("he" + "llo") == "hello"
res40: Boolean = true
How Scala’s == differs from Java’s
In Java, you can use == to compare both primitive and reference types.
On primitive types, Java’s == compares value equality, as in Scala. On
reference types, however, Java’s == compares reference equality, which
means the two variables point to the same object on the JVM’s heap.
Scala provides a facility for comparing reference equality, as well,
under the name eq. However, eq and its opposite, ne, only apply to
objects that directly map to Java objects. The full details about eq and
ne are given in Sections 11.1 and 11.2. Also, see Chapter 30 on how to
write a good equals method.
5.9
Operator precedence and associativity
Operator precedence determines which parts of an expression are evaluated
before the other parts. For example, the expression 2 + 2 * 7 evaluates to 16,
not 28, because the * operator has a higher precedence than the + operator.
Thus the multiplication part of the expression is evaluated before the addition
part. You can of course use parentheses in expressions to clarify evaluation
order or to override precedence. For example, if you really wanted the result
of the expression above to be 28, you could write the expression like this:
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(2 + 2) * 7
Given that Scala doesn’t have operators, per se, just a way to use methods
in operator notation, you may be wondering how operator precedence works.
Scala decides precedence based on the first character of the methods used in
operator notation (there’s one exception to this rule, which will be discussed
in the following pages). If the method name starts with a *, for example,
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it will have a higher precedence than a method that starts with a +. Thus
2 + 2 * 7 will be evaluated as 2 + (2 * 7). Similarly, a +++ b *** c (in which
a, b, and c are variables, and +++ and *** are methods) will be evaluated
a +++ (b *** c), because the *** method has a higher precedence than the
+++ method.
Table 5.3 · Operator precedence
(all other special characters)
*/%
+:
=!
<>
&
ˆ
|
(all letters)
(all assignment operators)
Table 5.3 shows the precedence given to the first character of a method
in decreasing order of precedence, with characters on the same line having
the same precedence. The higher a character is in this table, the higher the
precedence of methods that start with that character. Here’s an example that
illustrates the influence of precedence:
scala> 2 << 2 + 2
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res41: Int = 32
The << method starts with the character <, which appears lower in Table 5.3 than the character +, which is the first and only character of the +
method. Thus << will have lower precedence than +, and the expression
will be evaluated by first invoking the + method, then the << method, as in
2 << (2 + 2). 2 + 2 is 4, by our math, and 2 << 4 yields 32. If you swap the
operators, you’ll get a different result:
scala> 2 + 2 << 2
res42: Int = 16
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Since the first characters are the same as in the previous example, the
methods will be invoked in the same order. First the + method will be invoked, then the << method. So 2 + 2 will again yield 4, and 4 << 2 is 16.
The one exception to the precedence rule, alluded to earlier, concerns
assignment operators, which end in an equals character. If an operator ends
in an equals character (=), and the operator is not one of the comparison
operators <=, >=, ==, or !=, then the precedence of the operator is the same
as that of simple assignment (=). That is, it is lower than the precedence of
any other operator. For instance:
x *= y + 1
means the same as:
x *= (y + 1)
because *= is classified as an assignment operator whose precedence is lower
than +, even though the operator’s first character is *, which would suggest a
precedence higher than +.
When multiple operators of the same precedence appear side by side in
an expression, the associativity of the operators determines the way operators
are grouped. The associativity of an operator in Scala is determined by its
last character. As mentioned on page 83 of Chapter 3, any method that ends
in a ‘:’ character is invoked on its right operand, passing in the left operand.
Methods that end in any other character are the other way around: They are
invoked on their left operand, passing in the right operand. So a * b yields
a.*(b), but a ::: b yields b.:::(a).
No matter what associativity an operator has, however, its operands are
always evaluated left to right. So if a is an expression that is not just a simple
reference to an immutable value, then a ::: b is more precisely treated as
the following block:
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{ val x = a; b.:::(x) }
In this block a is still evaluated before b, and then the result of this evaluation
is passed as an operand to b’s ::: method.
This associativity rule also plays a role when multiple operators of the
same precedence appear side by side. If the methods end in ‘:’, they are
grouped right to left; otherwise, they are grouped left to right. For example,
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a ::: b ::: c is treated as a ::: (b ::: c). But a * b * c, by contrast, is
treated as (a * b) * c.
Operator precedence is part of the Scala language. You needn’t be afraid
to use it. Nevertheless, it is good style to use parentheses to clarify what
operators are operating upon what expressions. Perhaps the only precedence
you can truly count on other programmers knowing without looking up is
that multiplicative operators, *, /, and %, have a higher precedence than the
additive ones + and -. Thus even if a + b << c yields the result you want without parentheses, the extra clarity you get by writing (a + b) << c may reduce
the frequency with which your peers utter your name in operator notation,
for example, by shouting in disgust, “bills !*&ˆ%~ code!”.8
5.10
Rich wrappers
You can invoke many more methods on Scala’s basic types than were described in the previous sections. A few examples are shown in Table 5.4.
These methods are available via implicit conversions, a technique that will
be described in detail in Chapter 21. All you need to know for now is that for
each basic type described in this chapter, there is also a “rich wrapper” that
provides several additional methods. To see all the available methods on the
basic types, therefore, you should look at the API documentation on the rich
wrapper for each basic type. Those classes are listed in Table 5.5.
5.11
Conclusion
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The main take-aways from this chapter are that operators in Scala are method
calls, and that implicit conversions to rich variants exist for Scala’s basic
types that add even more useful methods. In the next chapter, we’ll show
you what it means to design objects in a functional style that gives new implementations of some of the operators that you have seen in this chapter.
8 By
now you should be able to figure out that given this code, the Scala compiler would
invoke (bills.!*&ˆ%~(code)).!().
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Table 5.4 · Some rich operations
Code
Result
0 max 5
0 min 5
-2.7 abs
-2.7 round
1.5 isInfinity
(1.0 / 0) isInfinity
4 to 6
"bob" capitalize
"robert" drop 2
5
0
2.7
-3L
false
true
Range(4, 5, 6)
"Bob"
"bert"
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Table 5.5 · Rich wrapper classes
Basic type
Rich wrapper
Byte
Short
Int
Long
Char
Float
Double
Boolean
String
scala.runtime.RichByte
scala.runtime.RichShort
scala.runtime.RichInt
scala.runtime.RichLong
scala.runtime.RichChar
scala.runtime.RichFloat
scala.runtime.RichDouble
scala.runtime.RichBoolean
scala.collection.immutable.StringOps
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134
Chapter 6
Functional Objects
With the understanding of Scala basics you’ve gained from previous chapters, you’re ready to design more full-featured classes in Scala. In this chapter, the emphasis is on classes that define functional objects, or objects that do
not have any mutable state. As a running example, we’ll create several variants of a class that models rational numbers as immutable objects. Along the
way, we’ll show you more aspects of object-oriented programming in Scala:
class parameters and constructors, methods and operators, private members,
overriding, checking preconditions, overloading, and self references.
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6.1
A specification for class Rational
A rational number is a number that can be expressed as a ratio dn , where n
and d are integers, except that d cannot be zero. n is called the numerator
2
and d the denominator. Examples of rational numbers are 12 , 23 , 112
239 , and 1 .
Compared to floating-point numbers, rational numbers have the advantage
that fractions are represented exactly, without rounding or approximation.
The class we’ll design in this chapter must model the behavior of rational
numbers, including allowing them to be added, subtracted, multiplied, and
divided. To add two rationals, you must first obtain a common denominator,
then add the two numerators. For example, to add 21 + 23 , you multiply both
parts of the left operand by 3 and both parts of the right operand by 2, which
gives you 36 + 46 . Adding the two numerators yields the result, 76 . To multiply two rational numbers, you can simply multiply their numerators and
2
multiply their denominators. Thus, 21 ∗ 25 gives 10
, which can be represented
1
more compactly in its “normalized” form as 5 . You divide by swapping the
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Section 6.2
Chapter 6 · Functional Objects
numerator and denominator of the right operand and then multiplying. For
instance 21 / 35 is the same as 12 ∗ 53 , or 65 .
One, maybe rather trivial, observation is that in mathematics, rational
numbers do not have mutable state. You can add one rational number to
another, but the result will be a new rational number. The original numbers will not have “changed.” The immutable Rational class we’ll design
in this chapter will have the same property. Each rational number will be
represented by one Rational object. When you add two Rational objects,
you’ll create a new Rational object to hold the sum.
This chapter will give you a glimpse of some of the ways Scala enables
you to write libraries that feel like native language support. For example, at
the end of this chapter you’ll be able to do this with class Rational:
scala> val oneHalf = new Rational(1, 2)
oneHalf: Rational = 1/2
scala> val twoThirds = new Rational(2, 3)
twoThirds: Rational = 2/3
scala> (oneHalf / 7) + (1 - twoThirds)
res0: Rational = 17/42
6.2
Constructing a Rational
A good place to start designing class Rational is to consider how client
programmers will create a new Rational object. Given we’ve decided to
make Rational objects immutable, we’ll require that clients provide all data
needed by an instance (in this case, a numerator and a denominator) when
they construct the instance. Thus, we will start the design with this:
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class Rational(n: Int, d: Int)
One of the first things to note about this line of code is that if a class doesn’t
have a body, you don’t need to specify empty curly braces (though you could,
of course, if you wanted to). The identifiers n and d in the parentheses after
the class name, Rational, are called class parameters. The Scala compiler
will gather up these two class parameters and create a primary constructor
that takes the same two parameters.
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Immutable object trade-offs
Immutable objects offer several advantages over mutable objects, and
one potential disadvantage. First, immutable objects are often easier to
reason about than mutable ones, because they do not have complex state
spaces that change over time. Second, you can pass immutable objects
around quite freely, whereas you may need to make defensive copies
of mutable objects before passing them to other code. Third, there is
no way for two threads concurrently accessing an immutable to corrupt
its state once it has been properly constructed, because no thread can
change the state of an immutable. Fourth, immutable objects make safe
hash table keys. If a mutable object is mutated after it is placed into a
HashSet, for example, that object may not be found the next time you
look into the HashSet.
The main disadvantage of immutable objects is that they sometimes
require that a large object graph be copied, whereas an update could
be done in its place. In some cases this can be awkward to express
and might also cause a performance bottleneck. As a result, it is not
uncommon for libraries to provide mutable alternatives to immutable
classes. For example, class StringBuilder is a mutable alternative to
the immutable String. We’ll give you more information on designing
mutable objects in Scala in Chapter 18.
Note
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This initial Rational example highlights a difference between Java and
Scala. In Java, classes have constructors, which can take parameters;
whereas in Scala, classes can take parameters directly. The Scala notation
is more concise—class parameters can be used directly in the body of the
class; there’s no need to define fields and write assignments that copy
constructor parameters into fields. This can yield substantial savings in
boilerplate code, especially for small classes.
The Scala compiler will compile any code you place in the class body,
which isn’t part of a field or a method definition, into the primary constructor.
For example, you could print a debug message like this:
class Rational(n: Int, d: Int) {
println("Created " + n + "/" + d)
}
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Given this code, the Scala compiler would place the call to println into
Rational’s primary constructor. The println call will, therefore, print its
debug message whenever you create a new Rational instance:
scala> new Rational(1, 2)
Created 1/2
res0: Rational = Rational@2591e0c9
6.3
Reimplementing the toString method
When we created an instance of Rational in the previous example, the interpreter printed “Rational@90110a”. The interpreter obtained this somewhat funny looking string by calling toString on the Rational object. By
default, class Rational inherits the implementation of toString defined
in class java.lang.Object, which just prints the class name, an @ sign,
and a hexadecimal number. The result of toString is primarily intended
to help programmers by providing information that can be used in debug
print statements, log messages, test failure reports, and interpreter and debugger output. The result currently provided by toString is not especially
helpful because it doesn’t give any clue about the rational number’s value.
A more useful implementation of toString would print out the values of
the Rational’s numerator and denominator. You can override the default
implementation by adding a method toString to class Rational, like this:
class Rational(n: Int, d: Int) {
override def toString = n + "/" + d
}
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The override modifier in front of a method definition signals that a previous method definition is overridden (more on this in Chapter 10). Since
Rational numbers will display nicely now, we removed the debug println
statement we put into the body of previous version of class Rational. You
can test the new behavior of Rational in the interpreter:
scala> val x = new Rational(1, 3)
x: Rational = 1/3
scala> val y = new Rational(5, 7)
y: Rational = 5/7
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Section 6.4
6.4
Chapter 6 · Functional Objects
Checking preconditions
As a next step, we will turn our attention to a problem with the current behavior of the primary constructor. As mentioned at the beginning of this chapter,
rational numbers may not have a zero in the denominator. Currently, however, the primary constructor accepts a zero passed as d:
scala> new Rational(5, 0)
res1: Rational = 5/0
One of the benefits of object-oriented programming is that it allows you
to encapsulate data inside objects so that you can ensure the data is valid
throughout its lifetime. In the case of an immutable object such as Rational,
this means that you should ensure the data is valid when the object is constructed. Given that a zero denominator is an invalid state for a Rational
number, you should not let a Rational be constructed if a zero is passed in
the d parameter.
The best way to approach this problem is to define as a precondition of
the primary constructor that d must be non-zero. A precondition is a constraint on values passed into a method or constructor, a requirement which
callers must fulfill. One way to do that is to use require,1 like this:
class Rational(n: Int, d: Int) {
require(d != 0)
override def toString = n + "/" + d
}
The require method takes one boolean parameter. If the passed value is
true, require will return normally. Otherwise, require will prevent the object from being constructed by throwing an IllegalArgumentException.
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6.5
Adding fields
Now that the primary constructor is properly enforcing its precondition, we
will turn our attention to supporting addition. To do so, we’ll define a public
add method on class Rational that takes another Rational as a parameter. To keep Rational immutable, the add method must not add the passed
1 The require
method is defined in standalone object, Predef. As mentioned in Section 4.4, Predef’s members are imported automatically into every Scala source file.
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rational number to itself. Rather, it must create and return a new Rational
that holds the sum. You might think you could write add this way:
class Rational(n: Int, d: Int) { // This won't compile
require(d != 0)
override def toString = n + "/" + d
def add(that: Rational): Rational =
new Rational(n * that.d + that.n * d, d * that.d)
}
However, given this code the compiler will complain:
:11: error: value d is not a member of Rational
new Rational(n * that.d + that.n * d, d * that.d)
ˆ
:11: error: value d is not a member of Rational
new Rational(n * that.d + that.n * d, d * that.d)
ˆ
Although class parameters n and d are in scope in the code of your add
method, you can only access their value on the object on which add was
invoked. Thus, when you say n or d in add’s implementation, the compiler is
happy to provide you with the values for these class parameters. But it won’t
let you say that.n or that.d because that does not refer to the Rational
object on which add was invoked.2 To access the numerator and denominator
on that, you’ll need to make them into fields. Listing 6.1 shows how you
could add these fields to class Rational.3
In the version of Rational shown in Listing 6.1, we added two fields
named numer and denom, and initialized them with the values of class parameters n and d.4 We also changed the implementation of toString and
add so that they use the fields, not the class parameters. This version of class
Rational compiles. You can test it by adding some rational numbers:
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2 Actually,
you could add a Rational to itself, in which case that would refer to the
object on which add was invoked. But because you can pass any Rational object to add, the
compiler still won’t let you say that.n.
3 In Section 10.6 you’ll find out about parametric fields, which provide a shorthand for
writing the same code.
4 Even though n and d are used in the body of the class, given they are only used inside
constructors, the Scala compiler will not emit fields for them. Thus, given this code the Scala
compiler will generate a class with two Int fields, one for numer and one for denom.
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class Rational(n: Int, d: Int) {
require(d != 0)
val numer: Int = n
val denom: Int = d
override def toString = numer + "/" + denom
def add(that: Rational): Rational =
new Rational(
numer * that.denom + that.numer * denom,
denom * that.denom
)
}
Listing 6.1 · Rational with fields.
scala> val oneHalf = new Rational(1, 2)
oneHalf: Rational = 1/2
scala> val twoThirds = new Rational(2, 3)
twoThirds: Rational = 2/3
scala> oneHalf add twoThirds
res2: Rational = 7/6
One other thing you can do now that you couldn’t do before is access the
numerator and denominator values from outside the object. Simply access
the public numer and denom fields, like this:
scala> val r = new Rational(1, 2)
r: Rational = 1/2
scala> r.numer
res3: Int = 1
scala> r.denom
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res4: Int = 2
6.6
Self references
The keyword this refers to the object instance on which the currently executing method was invoked, or if used in a constructor, the object instance
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being constructed. As an example, consider adding a method, lessThan,
which tests whether the given Rational is smaller than a parameter:
def lessThan(that: Rational) =
this.numer * that.denom < that.numer * this.denom
Here, this.numer refers to the numerator of the object on which lessThan
was invoked. You can also leave off the this prefix and write just numer;
the two notations are equivalent.
As an example of where you can’t do without this, consider adding a
max method to class Rational that returns the greater of the given rational
number and an argument:
def max(that: Rational) =
if (this.lessThan(that)) that else this
Here, the first this is redundant. You could have left it off and written:
lessThan(that). But the second this represents the result of the method
in the case where the test returns false; were you to omit it, there would be
nothing left to return!
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6.7
Auxiliary constructors
Sometimes you need multiple constructors in a class. In Scala, constructors other than the primary constructor are called auxiliary constructors. For
example, a rational number with a denominator of 1 can be written more succinctly as simply the numerator. Instead of 15 , for example, you can just write
5. It might be nice, therefore, if instead of writing new Rational(5, 1),
client programmers could simply write new Rational(5). This would require adding an auxiliary constructor to Rational that takes only one argument, the numerator, with the denominator predefined to be 1. Listing 6.2
shows what that would look like.
Auxiliary constructors in Scala start with def this(...). The body
of Rational’s auxiliary constructor merely invokes the primary constructor,
passing along its lone argument, n, as the numerator and 1 as the denominator. You can see the auxiliary constructor in action by typing the following
into the interpreter:
scala> val y = new Rational(3)
y: Rational = 3/1
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class Rational(n: Int, d: Int) {
require(d != 0)
val numer: Int = n
val denom: Int = d
def this(n: Int) = this(n, 1) // auxiliary constructor
override def toString = numer + "/" + denom
def add(that: Rational): Rational =
new Rational(
numer * that.denom + that.numer * denom,
denom * that.denom
)
}
Listing 6.2 · Rational with an auxiliary constructor.
In Scala, every auxiliary constructor must invoke another constructor of
the same class as its first action. In other words, the first statement in every
auxiliary constructor in every Scala class will have the form “this(. . . )”.
The invoked constructor is either the primary constructor (as in the Rational
example), or another auxiliary constructor that comes textually before the
calling constructor. The net effect of this rule is that every constructor invocation in Scala will end up eventually calling the primary constructor of the
class. The primary constructor is thus the single point of entry of a class.
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Note
If you’re familiar with Java, you may wonder why Scala’s rules for
constructors are a bit more restrictive than Java’s. In Java, a constructor
must either invoke another constructor of the same class, or directly invoke
a constructor of the superclass, as its first action. In a Scala class, only the
primary constructor can invoke a superclass constructor. The increased
restriction in Scala is really a design trade-off that needed to be paid in
exchange for the greater conciseness and simplicity of Scala’s constructors
compared to Java’s. Superclasses and the details of how constructor
invocation and inheritance interact will be explained in Chapter 10.
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Section 6.8
6.8
Chapter 6 · Functional Objects
Private fields and methods
In the previous version of Rational, we simply initialized numer with n and
denom with d. As a result, the numerator and denominator of a Rational can
be larger than needed. For example, the fraction 66
42 could be normalized to
11
an equivalent reduced form, 7 , but Rational’s primary constructor doesn’t
currently do this:
scala> new Rational(66, 42)
res5: Rational = 66/42
To normalize in this way, you need to divide the numerator and denominator
by their greatest common divisor. For example, the greatest common divisor
of 66 and 42 is 6. (In other words, 6 is the largest integer that divides evenly
into both 66 and 42.) Dividing both the numerator and denominator of 66
42 by
6 yields its reduced form, 11
.
Listing
6.3
shows
one
way
to
do
this:
7
class Rational(n: Int, d: Int) {
require(d != 0)
private val g = gcd(n.abs, d.abs)
val numer = n / g
val denom = d / g
def this(n: Int) = this(n, 1)
def add(that: Rational): Rational =
new Rational(
numer * that.denom + that.numer * denom,
denom * that.denom
)
override def toString = numer + "/" + denom
private def gcd(a: Int, b: Int): Int =
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if (b == 0) a else gcd(b, a % b)
}
Listing 6.3 · Rational with a private field and method.
In this version of Rational, we added a private field, g, and modified the
initializers for numer and denom. (An initializer is the code that initializes
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Section 6.9
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a variable; for example, the “n / g” that initializes numer.) Because g is
private, it can be accessed inside the body of the class, but not outside. We
also added a private method, gcd, which calculates the greatest common
divisor of two passed Ints. For example, gcd(12, 8) is 4. As you saw in
Section 4.1, to make a field or method private you simply place the private
keyword in front of its definition. The purpose of the private “helper method”
gcd is to factor out code needed by some other part of the class, in this case,
the primary constructor. To ensure g is always positive, we pass the absolute
value of n and d, which we obtain by invoking abs on them, a method you
can invoke on any Int to get its absolute value.
The Scala compiler will place the code for the initializers of Rational’s
three fields into the primary constructor in the order in which they appear
in the source code. Thus, g’s initializer, gcd(n.abs, d.abs), will execute
before the other two, because it appears first in the source. Field g will be
initialized with the result, the greatest common divisor of the absolute value
of the class parameters, n and d. Field g is then used in the initializers of
numer and denom. By dividing n and d by their greatest common divisor, g,
every Rational will be constructed in its normalized form:
scala> new Rational(66, 42)
res6: Rational = 11/7
6.9
Defining operators
The current implementation of Rational addition is OK, but could be made
more convenient to use. You might ask yourself why you can write:
x + y
if x and y are integers or floating-point numbers, but you have to write:
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x.add(y)
or at least:
x add y
if they are rational numbers. There’s no convincing reason why this should
be so. Rational numbers are numbers just like other numbers. In a mathematical sense they are even more natural than, say, floating-point numbers.
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Why should you not use the natural arithmetic operators on them? In Scala
you can do this. In the rest of this chapter, we’ll show you how.
The first step is to replace add by the usual mathematical symbol. This
is straightforward, as + is a legal identifier in Scala. We can simply define a
method with + as its name. While we’re at it, we may as well implement a
method named * that performs multiplication. The result is shown in Listing 6.4:
class Rational(n: Int, d: Int) {
require(d != 0)
private val g = gcd(n.abs, d.abs)
val numer = n / g
val denom = d / g
def this(n: Int) = this(n, 1)
def + (that: Rational): Rational =
new Rational(
numer * that.denom + that.numer * denom,
denom * that.denom
)
def * (that: Rational): Rational =
new Rational(numer * that.numer, denom * that.denom)
override def toString = numer + "/" + denom
private def gcd(a: Int, b: Int): Int =
if (b == 0) a else gcd(b, a % b)
}
Listing 6.4 · Rational with operator methods.
With class Rational defined in this manner, you can now write:
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scala> val x = new Rational(1, 2)
x: Rational = 1/2
scala> val y = new Rational(2, 3)
y: Rational = 2/3
scala> x + y
res7: Rational = 7/6
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As always, the operator syntax on the last input line is equivalent to a method
call. You could also write:
scala> x.+(y)
res8: Rational = 7/6
but this is not as readable.
Another thing to note is that given Scala’s rules for operator precedence,
which were described in Section 5.9, the * method will bind more tightly
than the + method for Rationals. In other words, expressions involving
+ and * operations on Rationals will behave as expected. For example,
x + x * y will execute as x + (x * y), not (x + x) * y:
scala> x + x * y
res9: Rational = 5/6
scala> (x + x) * y
res10: Rational = 2/3
scala> x + (x * y)
res11: Rational = 5/6
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6.10
Identifiers in Scala
You have now seen the two most important ways to form an identifier in
Scala: alphanumeric and operator. Scala has very flexible rules for forming
identifiers. Besides the two forms you have seen there are also two others.
All four forms of identifier formation are described in this section.
An alphanumeric identifier starts with a letter or underscore, which can
be followed by further letters, digits, or underscores. The ‘$’ character also
counts as a letter; however, it is reserved for identifiers generated by the
Scala compiler. Identifiers in user programs should not contain ‘$’ characters, even though it will compile; if they do, this might lead to name clashes
with identifiers generated by the Scala compiler.
Scala follows Java’s convention of using camel-case5 identifiers, such as
toString and HashSet. Although underscores are legal in identifiers, they
are not used that often in Scala programs, in part to be consistent with Java,
5 This style of naming identifiers is called camel case because the identifiersHaveHumps
consisting of the embedded capital letters.
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but also because underscores have many other non-identifier uses in Scala
code. As a result, it is best to avoid identifiers like to_string, __init__,
or name_. Camel-case names of fields, method parameters, local variables,
and functions should start with a lower case letter, for example: length,
flatMap, and s. Camel-case names of classes and traits should start with an
upper case letter, for example: BigInt, List, and UnbalancedTreeMap.6
Note
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One consequence of using a trailing underscore in an identifier is that if
you attempt, for example, to write a declaration like this,
“val name_: Int = 1”, you’ll get a compiler error. The compiler will
think you are trying to declare a val named “name_:”. To get this to
compile, you would need to insert an extra space before the colon, as in:
“val name_ : Int = 1”.
One way in which Scala’s conventions depart from Java’s involves constant names. In Scala, the word constant does not just mean val. Even
though a val does remain constant after it is initialized, it is still a variable.
For example, method parameters are vals, but each time the method is called
those vals can hold different values. A constant is more permanent. For example, scala.math.Pi is defined to be the double value closest to the real
value of π, the ratio of a circle’s circumference to its diameter. This value
is unlikely to change ever; thus, Pi is clearly a constant. You can also use
constants to give names to values that would otherwise be magic numbers in
your code: literal values with no explanation, which in the worst case appear
in multiple places. You may also want to define constants for use in pattern
matching, a use case that will be described in Section 15.2. In Java, the convention is to give constants names that are all upper case, with underscores
separating the words, such as MAX_VALUE or PI. In Scala, the convention is
merely that the first character should be upper case. Thus, constants named
in the Java style, such as X_OFFSET, will work as Scala constants, but the
Scala convention is to use camel case for constants, such as XOffset.
An operator identifier consists of one or more operator characters. Operator characters are printable ASCII characters such as +, :, ?, ~ or #.7 Here
6 In
Section 16.5, you’ll see that sometimes you may want to give a special kind of class
known as a case class a name consisting solely of operator characters. For example, the Scala
API contains a class named ::, which facilitates pattern matching on Lists.
7 More precisely, an operator character belongs to the Unicode set of mathematical symbols(Sm) or other symbols(So), or to the 7-bit ASCII characters that are not letters, digits,
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are some examples of operator identifiers:
+
++
:::
:->
The Scala compiler will internally “mangle” operator identifiers to turn
them into legal Java identifiers with embedded $ characters. For instance, the
identifier :-> would be represented internally as $colon$minus$greater.
If you ever wanted to access this identifier from Java code, you’d need to use
this internal representation.
Because operator identifiers in Scala can become arbitrarily long, there is
a small difference between Java and Scala. In Java, the input x<-y would be
parsed as four lexical symbols, so it would be equivalent to x < - y. In Scala,
<- would be parsed as a single identifier, giving x <- y. If you want the first
interpretation, you need to separate the < and the - characters by a space.
This is unlikely to be a problem in practice, as very few people would write
x<-y in Java without inserting spaces or parentheses between the operators.
A mixed identifier consists of an alphanumeric identifier, which is followed by an underscore and an operator identifier. For example, unary_+
used as a method name defines a unary + operator. Or, myvar_= used as
method name defines an assignment operator. In addition, the mixed identifier form myvar_= is generated by the Scala compiler to support properties
(more on that in Chapter 18).
A literal identifier is an arbitrary string enclosed in back ticks (` . . . `).
Some examples of literal identifiers are:
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`x`
``
`yield`
The idea is that you can put any string that’s accepted by the runtime
as an identifier between back ticks. The result is always a Scala identifier.
This works even if the name contained in the back ticks would be a Scala
reserved word. A typical use case is accessing the static yield method in
Java’s Thread class. You cannot write Thread.yield() because yield is
a reserved word in Scala. However, you can still name the method in back
ticks, e.g., Thread.`yield`().
parentheses, square brackets, curly braces, single or double quote, or an underscore, period,
semi-colon, comma, or back tick character.
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Chapter 6 · Functional Objects
Method overloading
Back to class Rational. With the latest changes, you can now do addition
and multiplication operations in a natural style on rational numbers. But one
thing still missing is mixed arithmetic. For instance, you cannot multiply a
rational number by an integer because the operands of * always have to be
Rationals. So for a rational number r you can’t write r * 2. You must write
r * new Rational(2), which is not as nice.
To make Rational even more convenient, we’ll add new methods to the
class that perform mixed addition and multiplication on rational numbers and
integers. While we’re at it, we’ll add methods for subtraction and division
too. The result is shown in Listing 6.5.
There are now two versions each of the arithmetic methods: one that
takes a rational as its argument and another that takes an integer. In other
words, each of these method names is overloaded because each name is
now being used by multiple methods. For example, the name + is used
by one method that takes a Rational and another that takes an Int. In a
method call, the compiler picks the version of an overloaded method that
correctly matches the types of the arguments. For instance, if the argument
y in x.+(y) is a Rational, the compiler will pick the method + that takes
a Rational parameter. But if the argument is an integer, the compiler will
pick the method + that takes an Int parameter instead. If you try this:
scala> val x = new Rational(2, 3)
x: Rational = 2/3
scala> x * x
res12: Rational = 4/9
scala> x * 2
res13: Rational = 4/3
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You’ll see that the * method invoked is determined in each case by the type
of the right operand.
Note
Scala’s process of overloaded method resolution is very similar to Java’s.
In every case, the chosen overloaded version is the one that best matches
the static types of the arguments. Sometimes there is no unique best
matching version; in that case the compiler will give you an “ambiguous
reference” error.
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class Rational(n: Int, d: Int) {
require(d != 0)
private val g = gcd(n.abs, d.abs)
val numer = n / g
val denom = d / g
def this(n: Int) = this(n, 1)
def + (that: Rational): Rational =
new Rational(
numer * that.denom + that.numer * denom,
denom * that.denom
)
def + (i: Int): Rational =
new Rational(numer + i * denom, denom)
def - (that: Rational): Rational =
new Rational(
numer * that.denom - that.numer * denom,
denom * that.denom
)
def - (i: Int): Rational =
new Rational(numer - i * denom, denom)
def * (that: Rational): Rational =
new Rational(numer * that.numer, denom * that.denom)
def * (i: Int): Rational =
new Rational(numer * i, denom)
def / (that: Rational): Rational =
new Rational(numer * that.denom, denom * that.numer)
def / (i: Int): Rational =
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new Rational(numer, denom * i)
override def toString = numer + "/" + denom
private def gcd(a: Int, b: Int): Int =
if (b == 0) a else gcd(b, a % b)
}
Listing 6.5 · Rational with overloaded methods.
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Section 6.12
6.12
Chapter 6 · Functional Objects
Implicit conversions
Now that you can write r * 2, you might also want to swap the operands, as
in 2 * r. Unfortunately this does not work yet:
scala> 2 * r
:10: error: overloaded method value * with
alternatives:
(x: Double)Double
(x: Float)Float
(x: Long)Long
(x: Int)Int
(x: Char)Int
(x: Short)Int
(x: Byte)Int
cannot be applied to (Rational)
2 * r
ˆ
The problem here is that 2 * r is equivalent to 2.*(r), so it is a method
call on the number 2, which is an integer. But the Int class contains no
multiplication method that takes a Rational argument—it couldn’t because
class Rational is not a standard class in the Scala library.
However, there is another way to solve this problem in Scala: You can
create an implicit conversion that automatically converts integers to rational
numbers when needed. Try adding this line in the interpreter:
scala> implicit def intToRational(x: Int) = new Rational(x)
This defines a conversion method from Int to Rational. The implicit
modifier in front of the method tells the compiler to apply it automatically in
a number of situations. With the conversion defined, you can now retry the
example that failed before:
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scala> val r = new Rational(2,3)
r: Rational = 2/3
scala> 2 * r
res15: Rational = 4/3
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For an implicit conversion to work, it needs to be in scope. If you place the
implicit method definition inside class Rational, it won’t be in scope in the
interpreter. For now, you’ll need to define it directly in the interpreter.
As you can glimpse from this example, implicit conversions are a very
powerful technique for making libraries more flexible and more convenient
to use. Because they are so powerful, they can also be easily misused. You’ll
find out more on implicit conversions, including ways to bring them into
scope where they are needed, in Chapter 21.
6.13
A word of caution
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As this chapter has demonstrated, creating methods with operator names and
defining implicit conversions can help you design libraries for which client
code is concise and easy to understand. Scala gives you a great deal of power
to design such easy-to-use libraries. But please bear in mind that with power
comes responsibility.
If used unartfully, both operator methods and implicit conversions can
give rise to client code that is hard to read and understand. Because implicit conversions are applied implicitly by the compiler, not explicitly written down in the source code, it can be non-obvious to client programmers
what implicit conversions are being applied. And although operator methods will usually make client code more concise, they will only make it more
readable to the extent client programmers will be able to recognize and remember the meaning of each operator.
The goal you should keep in mind as you design libraries is not merely
enabling concise client code, but readable, understandable client code. Conciseness will often be a big part of that readability, but you can take conciseness too far. By designing libraries that enable tastefully concise and at the
same time understandable client code, you can help those client programmers work productively.
6.14
Conclusion
In this chapter, you saw more aspects of classes in Scala. You saw how to
add parameters to a class, define several constructors, define operators as
methods, and customize classes so that they are natural to use. Maybe most
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importantly, you saw that defining and using immutable objects is a quite
natural way to code in Scala.
Although the final version of Rational shown in this chapter fulfills the
requirements set forth at the beginning of the chapter, it could still be improved. We will in fact return to this example later in the book. For example,
in Chapter 30, you’ll learn how to override equals and hashcode to allow
Rationals to behave better when compared with == or placed into hash tables. In Chapter 21, you’ll learn how to place implicit method definitions
in a companion object for Rational, so they can be more easily placed into
scope when client programmers are working with Rationals.
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Built-in Control Structures
Scala has only a handful of built-in control structures. The only control structures are if, while, for, try, match, and function calls. The reason Scala
has so few is that it has included function literals since its inception. Instead
of accumulating one higher-level control structure after another in the base
syntax, Scala accumulates them in libraries. (Chapter 9 will show precisely
how that is done.) This chapter will show those few control structures that
are built in.
One thing you will notice is that almost all of Scala’s control structures
result in some value. This is the approach taken by functional languages,
where programs are viewed as computing a value, thus the components of a
program should also compute values. You can also view this approach as the
logical conclusion of a trend already present in imperative languages. In imperative languages, function calls can return a value, even though having the
called function update an output variable passed as an argument would work
just as well. In addition, imperative languages often have a ternary operator
(such as the ?: operator of C, C++, and Java), which behaves exactly like
if, but results in a value. Scala adopts this ternary operator model, but calls
it if. In other words, Scala’s if can result in a value. Scala then continues
this trend by having for, try, and match also result in values.
Programmers can use these result values to simplify their code, just as
they use return values of functions. Without this facility, the programmer
must create temporary variables just to hold results that are calculated inside
a control structure. Removing these temporary variables makes the code a
little simpler, and it also prevents many bugs where you set the variable in
one branch but forget to set it in another.
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Chapter 7 · Built-in Control Structures
Overall, Scala’s basic control structures, minimal as they are, provide
all of the essentials from imperative languages. Further, they allow you to
shorten your code by consistently having result values. To show you how this
works, we’ll take a closer look at each of Scala’s basic control structures.
7.1
If expressions
Scala’s if works just like in many other languages. It tests a condition and
then executes one of two code branches depending on whether the condition
holds true. Here is a common example, written in an imperative style:
var filename = "default.txt"
if (!args.isEmpty)
filename = args(0)
This code declares a variable, filename, and initializes it to a default value.
It then uses an if expression to check whether any arguments were supplied
to the program. If so, it changes the variable to hold the value specified in
the argument list. If no arguments were supplied, it leaves the variable set to
the default value.
This code can be written more nicely because, as mentioned in Step 3
in Chapter 2, Scala’s if is an expression that results in a value. Listing 7.1
shows how you can accomplish the same effect as the previous example,
without using any vars:
val filename =
if (!args.isEmpty) args(0)
else "default.txt"
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Listing 7.1 · Scala’s idiom for conditional initialization.
This time, the if has two branches. If args is not empty, the initial element, args(0), is chosen; otherwise, the default value is chosen. The if
expression results in the chosen value, and the filename variable is initialized with that value. This code is slightly shorter, but its real advantage is
that it uses a val instead of a var. Using a val is the functional style, and it
helps you in much the same way as a final variable in Java. It tells readers
of the code that the variable will never change, saving them from scanning
all code in the variable’s scope to see if it ever changes.
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A second advantage to using a val instead of a var is that it better supports equational reasoning. The introduced variable is equal to the expression that computes it, assuming that expression has no side effects. Thus,
any time you are about to write the variable name, you could instead write
the expression. Instead of println(filename), for example, you could just
write this:
println(if (!args.isEmpty) args(0) else "default.txt")
The choice is yours. You can write it either way. Using vals helps you safely
make this kind of refactoring as your code evolves over time.
Look for opportunities to use vals. They can make your
code both easier to read and easier to refactor.
7.2
While loops
Scala’s while loop behaves as in other languages. It has a condition and a
body, and the body is executed over and over as long as the condition holds
true. Listing 7.2 shows an example:
def gcdLoop(x: Long, y: Long): Long = {
var a = x
var b = y
while (a != 0) {
val temp = a
a = b % a
b = temp
}
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b
}
Listing 7.2 · Calculating greatest common divisor with a while loop.
Scala also has a do-while loop. This works like the while loop except
that it tests the condition after the loop body instead of before. Listing 7.3
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shows a Scala script that uses a do-while to echo lines read from the standard input, until an empty line is entered:
var line = ""
do {
line = readLine()
println("Read: " + line)
} while (line != "")
Listing 7.3 · Reading from the standard input with do-while.
The while and do-while constructs are called “loops,” not expressions,
because they don’t result in an interesting value. The type of the result is
Unit. It turns out that a value (and in fact, only one value) exists whose type
is Unit. It is called the unit value and is written (). The existence of () is
how Scala’s Unit differs from Java’s void. Try this in the interpreter:
scala> def greet() = { println("hi") }
greet: ()Unit
scala> () == greet()
hi
res0: Boolean = true
Because no equals sign precedes its body, greet is defined to be a procedure with a result type of Unit. Therefore, greet returns the unit value, ().
This is confirmed in the next line: comparing the greet’s result for equality
with the unit value, (), yields true.
One other construct that results in the unit value, which is relevant here,
is reassignment to vars. For example, were you to attempt to read lines in
Scala using the following while loop idiom from Java (and C and C++),
you’ll run into trouble:
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var line = ""
while ((line = readLine()) != "") // This doesn't work!
println("Read: " + line)
When you compile this code, Scala will give you a warning that comparing
values of type Unit and String using != will always yield true. Whereas
in Java, assignment results in the value assigned (in this case a line from
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the standard input), in Scala assignment always results in the unit value, ().
Thus, the value of the assignment “line = readLine()” will always be ()
and never be "". As a result, this while loop’s condition will never be false,
and the loop will, therefore, never terminate.
Because the while loop results in no value, it is often left out of pure
functional languages. Such languages have expressions, not loops. Scala
includes the while loop nonetheless because sometimes an imperative solution can be more readable, especially to programmers with a predominantly
imperative background. For example, if you want to code an algorithm that
repeats a process until some condition changes, a while loop can express it
directly while the functional alternative, which likely uses recursion, may be
less obvious to some readers of the code.
For example, Listing 7.4 shows an alternate way to determine a greatest
common divisor of two numbers.1 Given the same two values for x and
y, the gcd function shown in Listing 7.4 will return the same result as the
gcdLoop function, shown in Listing 7.2. The difference between these two
approaches is that gcdLoop is written in an imperative style, using vars and
and a while loop, whereas gcd is written in a more functional style that
involves recursion (gcd calls itself) and requires no vars.
def gcd(x: Long, y: Long): Long =
if (y == 0) x else gcd(y, x % y)
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Listing 7.4 · Calculating greatest common divisor with recursion.
In general, we recommend you challenge while loops in your code in the
same way you challenge vars. In fact, while loops and vars often go hand
in hand. Because while loops don’t result in a value, to make any kind of
difference to your program, a while loop will usually either need to update
vars or perform I/O. You can see this in action in the gcdLoop example
shown previously. As that while loop does its business, it updates vars a
and b. Thus, we suggest you be a bit suspicious of while loops in your code.
If there isn’t a good justification for a particular while or do-while loop,
try to find a way to do the same thing without it.
1 The gcd
function shown in Listing 7.4 uses the same approach used by the like-named
function, first shown in Listing 6.3, to calculate greatest common divisors for class Rational.
The main difference is that instead of Ints the gcd of Listing 7.4 works with Longs.
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For expressions
Scala’s for expression is a Swiss army knife of iteration. It lets you combine
a few simple ingredients in different ways to express a wide variety of iterations. Simple uses enable common tasks such as iterating through a sequence
of integers. More advanced expressions can iterate over multiple collections
of different kinds, filter out elements based on arbitrary conditions, and produce new collections.
Iteration through collections
The simplest thing you can do with for is to iterate through all the elements
of a collection. For example, Listing 7.5 shows some code that prints out
all files in the current directory. The I/O is performed using the Java API.
First, we create a java.io.File on the current directory, ".", and call its
listFiles method. This method returns an array of File objects, one per
directory and file contained in the current directory. We store the resulting
array in the filesHere variable.
val filesHere = (new java.io.File(".")).listFiles
for (file <- filesHere)
println(file)
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Listing 7.5 · Listing files in a directory with a for expression.
With the “file <- filesHere” syntax, which is called a generator, we
iterate through the elements of filesHere. In each iteration, a new val
named file is initialized with an element value. The compiler infers the
type of file to be File, because filesHere is an Array[File]. For each
iteration, the body of the for expression, println(file), will be executed.
Because File’s toString method yields the name of the file or directory, the
names of all the files and directories in the current directory will be printed.
The for expression syntax works for any kind of collection, not just
arrays.2 One convenient special case is the Range type, which you briefly
2 To be precise, the expression to the right of the <- symbol in a for expression can be
any type that has certain methods (in this case foreach) with appropriate signatures. Details
on how the Scala compiler processes for expressions are described in Chapter 23.
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saw in Table 5.4 on page 134. You can create Ranges using syntax like
“1 to 5” and can iterate through them with a for. Here is a simple example:
scala> for (i <- 1 to 4)
println("Iteration " + i)
Iteration 1
Iteration 2
Iteration 3
Iteration 4
If you don’t want to include the upper bound of the range in the values that
are iterated over, use until instead of to:
scala> for (i <- 1 until 4)
println("Iteration " + i)
Iteration 1
Iteration 2
Iteration 3
Iterating through integers like this is common in Scala, but not nearly as
much as in other languages. In other languages, you might use this facility
to iterate through an array, like this:
// Not common in Scala...
for (i <- 0 to filesHere.length - 1)
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println(filesHere(i))
This for expression introduces a variable i, sets it in turn to each integer
between 0 and filesHere.length - 1, and executes the body of the for
expression for each setting of i. For each setting of i, the i’th element of
filesHere is extracted and processed.
The reason this kind of iteration is less common in Scala is that you can
just iterate over the collection directly. When you do, your code becomes
shorter and you sidestep many of the off-by-one errors that can arise when
iterating through arrays. Should you start at 0 or 1? Should you add -1, +1,
or nothing to the final index? Such questions are easily answered, but also
easily answered wrong. It is safer to avoid such questions entirely.
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Filtering
Sometimes you don’t want to iterate through a collection in its entirety; you
want to filter it down to some subset. You can do this with a for expression
by adding a filter, an if clause inside the for’s parentheses. For example,
the code shown in Listing 7.6 lists only those files in the current directory
whose names end with “.scala”:
val filesHere = (new java.io.File(".")).listFiles
for (file <- filesHere if file.getName.endsWith(".scala"))
println(file)
Listing 7.6 · Finding .scala files using a for with a filter.
You could alternatively accomplish the same goal with this code:
for (file <- filesHere)
if (file.getName.endsWith(".scala"))
println(file)
This code yields the same output as the previous code, and likely looks more
familiar to programmers with an imperative background. The imperative
form, however, is only an option because this particular for expression is
executed for its printing side-effects and results in the unit value (). As
demonstrated later in this section, the for expression is called an “expression” because it can result in an interesting value, a collection whose type is
determined by the for expression’s <- clauses.
You can include more filters if you want. Just keep adding if clauses.
For example, to be extra defensive, the code in Listing 7.7 prints only files
and not directories. It does so by adding a filter that checks the file’s
isFile method.
for (
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file <- filesHere
if file.isFile
if file.getName.endsWith(".scala")
) println(file)
Listing 7.7 · Using multiple filters in a for expression.
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Nested iteration
If you add multiple <- clauses, you will get nested “loops.” For example, the for expression shown in Listing 7.8 has two nested loops. The
outer loop iterates through filesHere, and the inner loop iterates through
fileLines(file) for any file that ends with .scala.
def fileLines(file: java.io.File) =
scala.io.Source.fromFile(file).getLines().toList
def grep(pattern: String) =
for (
file <- filesHere
if file.getName.endsWith(".scala");
line <- fileLines(file)
if line.trim.matches(pattern)
) println(file + ": " + line.trim)
grep(".*gcd.*")
Listing 7.8 · Using multiple generators in a for expression.
If you prefer, you can use curly braces instead of parentheses to surround
the generators and filters. One advantage to using curly braces is that you can
leave off some of the semicolons that are needed when you use parentheses
because, as explained in Section 4.2, the Scala compiler will not infer semicolons while inside parentheses.
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Mid-stream variable bindings
Note that the previous code repeats the expression line.trim. This is a
non-trivial computation, so you might want to only compute it once. You
can do this by binding the result to a new variable using an equals sign (=).
The bound variable is introduced and used just like a val, only with the val
keyword left out. Listing 7.9 shows an example.
In Listing 7.9, a variable named trimmed is introduced halfway through
the for expression. That variable is initialized to the result of line.trim.
The rest of the for expression then uses the new variable in two places, once
in an if and once in println.
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def grep(pattern: String) =
for {
file <- filesHere
if file.getName.endsWith(".scala")
line <- fileLines(file)
trimmed = line.trim
if trimmed.matches(pattern)
} println(file + ": " + trimmed)
grep(".*gcd.*")
Listing 7.9 · Mid-stream assignment in a for expression.
Producing a new collection
While all of the examples so far have operated on the iterated values and then
forgotten them, you can also generate a value to remember for each iteration.
To do so, you prefix the body of the for expression by the keyword yield.
For example, here is a function that identifies the .scala files and stores
them in an array:
def scalaFiles =
for {
file <- filesHere
if file.getName.endsWith(".scala")
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} yield file
Each time the body of the for expression executes, it produces one value,
in this case simply file. When the for expression completes, the result
will include all of the yielded values contained in a single collection. The
type of the resulting collection is based on the kind of collections processed
in the iteration clauses. In this case the result is an Array[File], because
filesHere is an array and the type of the yielded expression is File.
Be careful, by the way, where you place the yield keyword. The syntax
of a for-yield expression is like this:
for clauses yield body
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fore the last expression of the block. Avoid the temptation to write things
like this:
for (file <- filesHere if file.getName.endsWith(".scala")) {
yield file
// Syntax error!
}
For example, the for expression shown in Listing 7.10 first transforms
the Array[File] named filesHere, which contains all files in the current
directory, to one that contains only .scala files. For each of these it generates an Iterator[String], the result of the fileLines method, whose
definition is shown in Listing 7.8. An Iterator offers methods next and
hasNext that allow you to iterate over a collection of elements. This initial iterator is transformed into another Iterator[String] containing only
trimmed lines that include the substring "for". Finally, for each of these, an
integer length is yielded. The result of this for expression is an Array[Int]
containing those lengths.
val forLineLengths =
for {
file <- filesHere
if file.getName.endsWith(".scala")
line <- fileLines(file)
trimmed = line.trim
if trimmed.matches(".*for.*")
} yield trimmed.length
Listing 7.10 · Transforming an Array[File] to Array[Int] with a for.
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At this point, you have seen all the major features of Scala’s for expression, but we went through them rather quickly. A more thorough coverage
of for expressions is given in Chapter 23.
7.4
Exception handling with try expressions
Scala’s exceptions behave just like in many other languages. Instead of returning a value in the normal way, a method can terminate by throwing an
exception. The method’s caller can either catch and handle that exception,
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or it can itself simply terminate, in which case the exception propagates to
the caller’s caller. The exception propagates in this way, unwinding the call
stack, until a method handles it or there are no more methods left.
Throwing exceptions
Throwing an exception in Scala looks the same as in Java. You create an
exception object and then throw it with the throw keyword:
throw new IllegalArgumentException
Although it may seem somewhat paradoxical, in Scala, throw is an expression that has a result type. Here’s an example where result type matters:
val half =
if (n % 2 == 0)
n / 2
else
throw new RuntimeException("n must be even")
What happens here is that if n is even, half will be initialized to half of n.
If n is not even, an exception will be thrown before half can be initialized
to anything at all. Because of this, it is safe to treat a thrown exception as
any kind of value whatsoever. Any context that tries to use the return from a
throw will never get to do so, and thus no harm will come.
Technically, an exception throw has type Nothing. You can use a throw
as an expression even though it will never actually evaluate to anything. This
little bit of technical gymnastics might sound weird, but is frequently useful
in cases like the previous example. One branch of an if computes a value,
while the other throws an exception and computes Nothing. The type of
the whole if expression is then the type of that branch which does compute
something. Type Nothing is discussed further in Section 11.3.
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Catching exceptions
You catch exceptions using the syntax shown in Listing 7.11 The syntax for
catch clauses was chosen for its consistency with an important part of Scala:
pattern matching. Pattern matching, a powerful feature, is described briefly
in this chapter and in more detail in Chapter 15.
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import java.io.FileReader
import java.io.FileNotFoundException
import java.io.IOException
try {
val f = new FileReader("input.txt")
// Use and close file
} catch {
case ex: FileNotFoundException => // Handle missing file
case ex: IOException => // Handle other I/O error
}
Listing 7.11 · A try-catch clause in Scala.
The behavior of this try-catch expression is the same as in other languages with exceptions. The body is executed, and if it throws an exception,
each catch clause is tried in turn. In this example, if the exception is of
type FileNotFoundException, the first clause will execute. If it is of type
IOException, the second clause will execute. If the exception is of neither
type, the try-catch will terminate and the exception will propagate further.
Note
One difference you’ll quickly notice in Scala is that, unlike Java, Scala
does not require you to catch checked exceptions or declare them in a
throws clause. You can declare a throws clause if you wish with the
@throws annotation, but it is not required. See Section 31.2 for more
information on @throws.
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The finally clause
You can wrap an expression with a finally clause if you want to cause some
code to execute no matter how the expression terminates. For example, you
might want to be sure an open file gets closed even if a method exits by
throwing an exception. Listing 7.12 shows an example.3
3 Although
you must always surround the case statements of a catch clause in parentheses, try and finally do not require parentheses if they contain only one expression. For
example, you could write: try t() catch { case e: Exception => ... } finally f().
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import java.io.FileReader
val file = new FileReader("input.txt")
try {
// Use the file
} finally {
file.close()
// Be sure to close the file
}
Listing 7.12 · A try-finally clause in Scala.
Note
Listing 7.12 shows the idiomatic way to ensure a non-memory resource,
such as a file, socket, or database connection, is closed. First you acquire
the resource. Then you start a try block in which you use the resource.
Lastly, you close the resource in a finally block. This idiom is the same
in Scala as in Java; alternatively, in Scala you can employ a technique
called the loan pattern to achieve the same goal more concisely. The loan
pattern will be described in Section 9.4.
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Yielding a value
As with most other Scala control structures, try-catch-finally results in
a value. For example, Listing 7.13 shows how you can try to parse a URL
but use a default value if the URL is badly formed. The result is that of
the try clause if no exception is thrown, or the relevant catch clause if an
exception is thrown and caught. If an exception is thrown but not caught, the
expression has no result at all. The value computed in the finally clause, if
there is one, is dropped. Usually finally clauses do some kind of clean up,
such as closing a file. Normally, they should not change the value computed
in the main body or a catch clause of the try.
If you’re familiar with Java, it’s worth noting that Scala’s behavior differs
from Java only because Java’s try-finally does not result in a value. As
in Java, if a finally clause includes an explicit return statement, or throws
an exception, that return value or exception will “overrule” any previous one
that originated in the try block or one of its catch clauses. For example,
given this, rather contrived, function definition:
def f(): Int = try return 1 finally return 2
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import java.net.URL
import java.net.MalformedURLException
def urlFor(path: String) =
try {
new URL(path)
} catch {
case e: MalformedURLException =>
new URL("http://www.scala-lang.org")
}
Listing 7.13 · A catch clause that yields a value.
calling f() results in 2. By contrast, given:
def g(): Int = try 1 finally 2
calling g() results in 1. Both of these functions exhibit behavior that could
surprise most programmers, so it’s usually best to avoid returning values
from finally clauses. The best way to think of finally clauses is as a way
to ensure some side effect happens, such as closing an open file.
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7.5
Match expressions
Scala’s match expression lets you select from a number of alternatives, just
like switch statements in other languages. In general a match expression
lets you select using arbitrary patterns, which will be described in Chapter 15. The general form can wait. For now, just consider using match to
select among a number of alternatives.
As an example, the script in Listing 7.14 reads a food name from the
argument list and prints a companion to that food. This match expression
examines firstArg, which has been set to the first argument out of the argument list. If it is the string "salt", it prints "pepper", while if it is the
string "chips", it prints "salsa", and so on. The default case is specified with an underscore (_), a wildcard symbol frequently used in Scala as a
placeholder for a completely unknown value.
There are a few important differences from Java’s switch statement.
One is that any kind of constant, as well as other things, can be used in cases
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val firstArg = if (args.length > 0) args(0) else ""
firstArg match {
case "salt" => println("pepper")
case "chips" => println("salsa")
case "eggs" => println("bacon")
case _ => println("huh?")
}
Listing 7.14 · A match expression with side effects.
in Scala, not just the integer-type, enum, and string constants of Java’s case
statements. In Listing 7.14, the alternatives are strings. Another difference
is that there are no breaks at the end of each alternative. Instead the break
is implicit, and there is no fall through from one alternative to the next. The
common case—not falling through—becomes shorter, and a source of errors
is avoided because programmers can no longer fall through by accident.
The most significant difference from Java’s switch, however, may be
that match expressions result in a value. In the previous example, each alternative in the match expression prints out a value. It would work just as well
to yield the value rather than print it, as shown in Listing 7.15. The value that
results from this match expression is stored in the friend variable. Aside
from the code getting shorter (in number of tokens anyway), the code now
disentangles two separate concerns: first it chooses a food and then prints it.
val firstArg = if (!args.isEmpty) args(0) else ""
val friend =
firstArg match {
case "salt" => "pepper"
case "chips" => "salsa"
case "eggs" => "bacon"
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case _ => "huh?"
}
println(friend)
Listing 7.15 · A match expression that yields a value.
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Living without break and continue
You may have noticed that there has been no mention of break or continue.
Scala leaves out these commands because they do not mesh well with function literals, a feature described in the next chapter. It is clear what continue
means inside a while loop, but what would it mean inside a function literal?
While Scala supports both imperative and functional styles of programming,
in this case it leans slightly towards functional programming in exchange
for simplifying the language. Do not worry, though. There are many ways to
program without break and continue, and if you take advantage of function
literals, those alternatives can often be shorter than the original code.
The simplest approach is to replace every continue by an if and every break by a boolean variable. The boolean variable indicates whether
the enclosing while loop should continue. For example, suppose you are
searching through an argument list for a string that ends with “.scala” but
does not start with a hyphen. In Java you could—if you were quite fond of
while loops, break, and continue—write the following:
int i = 0;
// This is Java
boolean foundIt = false;
while (i < args.length) {
if (args[i].startsWith("-")) {
i = i + 1;
continue;
}
if (args[i].endsWith(".scala")) {
foundIt = true;
break;
}
i = i + 1;
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}
To transliterate this Java code directly to Scala, instead of doing an if
and then a continue, you could write an if that surrounds the entire remainder of the while loop. To get rid of the break, you would normally
add a boolean variable indicating whether to keep going, but in this case you
can reuse foundIt. Using both of these tricks, the code ends up looking as
shown in Listing 7.16.
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var i = 0
var foundIt = false
while (i < args.length && !foundIt) {
if (!args(i).startsWith("-")) {
if (args(i).endsWith(".scala"))
foundIt = true
}
i = i + 1
}
Listing 7.16 · Looping without break or continue.
This Scala code in Listing 7.16 is quite similar to the original Java code.
All the basic pieces are still there and in the same order. There are two
reassignable variables and a while loop. Inside the loop, there is a test that
i is less than args.length, a check for "-", and a check for ".scala".
If you wanted to get rid of the vars in Listing 7.16, one approach you
could try is to rewrite the loop as a recursive function. You could, for example, define a searchFrom function that takes an integer as an input, searches
forward from there, and then returns the index of the desired argument. Using this technique the code would look as shown in Listing 7.17:
def searchFrom(i: Int): Int =
if (i >= args.length) -1
else if (args(i).startsWith("-")) searchFrom(i + 1)
else if (args(i).endsWith(".scala")) i
else searchFrom(i + 1)
val i = searchFrom(0)
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Listing 7.17 · A recursive alternative to looping with vars.
The version in Listing 7.17 gives a human-meaningful name to what the
function does, and it uses recursion to substitute for looping. Each continue
is replaced by a recursive call with i + 1 as the argument, effectively skipping
to the next integer. Many people find this style of programming easier to
understand, once they get used to the recursion.
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Note
The Scala compiler will not actually emit a recursive function for the code
shown in Listing 7.17. Because all of the recursive calls are in tail-call
position, the compiler will generate code similar to a while loop. Each
recursive call will be implemented as a jump back to the beginning of the
function. Tail-call optimization is discussed in Section 8.9.
If after all this discussion you still feel the need to use break, there’s help
in Scala’s standard library. Class Breaks in package scala.util.control
offers a break method, which can be used to exit an enclosing block that’s
marked with breakable. Here is an example how this library-supplied
break method could be applied:
import scala.util.control.Breaks._
import java.io._
val in = new BufferedReader(new InputStreamReader(System.in))
breakable {
while (true) {
println("? ")
if (in.readLine() == "") break
}
}
This will repeatedly read non-empty lines from the standard input. Once the
user enters an empty line, control flow exits from the enclosing breakable,
and with it the while loop.
The Breaks class implements break by throwing an exception that is
caught by an enclosing application of the breakable method. Therefore,
the call to break does not need to be in the same method as the call to
breakable.
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7.7
Variable scope
Now that you’ve seen Scala’s built-in control structures, we’ll use them in
this section to explain how scoping works in Scala.
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Fast track for Java programmers
If you’re a Java programmer, you’ll find that Scala’s scoping rules are
almost identical to Java’s. One difference between Java and Scala is that
Scala allows you to define variables of the same name in nested scopes. So
if you’re a Java programmer, you may wish to at least skim this section.
Variable declarations in Scala programs have a scope that defines where
you can use the name. The most common example of scoping is that curly
braces generally introduce a new scope, so anything defined inside curly
braces leaves scope after the final closing brace.4 As an illustration, consider
the function shown in Listing 7.18.
The printMultiTable function shown in Listing 7.18 prints out a multiplication table.5 The first statement of this function introduces a variable
named i and initializes it to the integer 1. You can then use the name i for
the remainder of the function.
The next statement in printMultiTable is a while loop:
while (i <= 10) {
var j = 1
...
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}
You can use i here because it is still in scope. In the first statement inside that
while loop, you introduce another variable, this time named j, and again
initialize it to 1. Because the variable j was defined inside the open curly
brace of the while loop, it can be used only within that while loop. If you
were to attempt to do something with j after the closing curly brace of this
while loop, after the comment that says j, prod, and k are out of scope,
your program would not compile.
All variables defined in this example—i, j, prod, and k—are local variables. Such variables are “local” to the function in which they are defined.
Each time a function is invoked, a new set of its local variables is used.
Once a variable is defined, you can’t define a new variable with the same
name in the same scope. For example, the following script with two variables
named a in the same scope would not compile:
4 There are a few exceptions to this rule because in Scala you can sometimes use curly
braces in place of parentheses. One example of this kind of curly-brace use is the alternative
for expression syntax described in Section 7.3.
5 The printMultiTable function shown in Listing 7.18 is written in an imperative style.
We’ll refactor it into a functional style in the next section.
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Section 7.7
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def printMultiTable() = {
var i = 1
// only i in scope here
while (i <= 10) {
var j = 1
// both i and j in scope here
while (j <= 10) {
val prod = (i * j).toString
// i, j, and prod in scope here
var k = prod.length
// i, j, prod, and k in scope here
while (k < 4) {
print(" ")
k += 1
}
print(prod)
j += 1
}
// i and j still in scope; prod and k out of scope
println()
i += 1
}
// i still in scope; j, prod, and k out of scope
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}
Listing 7.18 · Variable scoping when printing a multiplication table.
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Section 7.7
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val a = 1
val a = 2 // Does not compile
println(a)
You can, on the other hand, define a variable in an inner scope that has the
same name as a variable in an outer scope. The following script would compile and run:
val a = 1;
{
val a = 2 // Compiles just fine
println(a)
}
println(a)
When executed, the script shown previously would print 2 then 1, because
the a defined inside the curly braces is a different variable, which is in scope
only until the closing curly brace.6 One difference to note between Scala and
Java is that Java will not let you create a variable in an inner scope that has
the same name as a variable in an outer scope. In a Scala program, an inner
variable is said to shadow a like-named outer variable, because the outer
variable becomes invisible in the inner scope.
You might have already noticed something that looks like shadowing in
the interpreter:
scala> val a = 1
a: Int = 1
scala> val a = 2
a: Int = 2
scala> println(a)
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2
In the interpreter, you can reuse variable names to your heart’s content.
Among other things, this allows you to change your mind if you made a
mistake when you defined a variable the first time in the interpreter. You
can do this because conceptually the interpreter creates a new nested scope
6 By the way, the semicolon is required in this case after the first definition of a because
Scala’s semicolon inference mechanism will not place one there.
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Section 7.8
Chapter 7 · Built-in Control Structures
for each new statement you type in. Thus, you could visualize the previous
interpreted code like this:
val a = 1;
{
val a = 2;
{
println(a)
}
}
This code will compile and run as a Scala script, and like the code typed
into the interpreter, will print 2. Keep in mind that such code can be very
confusing to readers, because variable names adopt new meanings in nested
scopes. It is usually better to choose a new, meaningful variable name rather
than to shadow an outer variable.
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7.8
Refactoring imperative-style code
To help you gain insight into the functional style, in this section we’ll refactor the imperative approach to printing a multiplication table shown in Listing 7.18. Our functional alternative is shown in Listing 7.19.
The imperative style reveals itself in Listing 7.18 in two ways. First,
invoking printMultiTable has a side effect: printing a multiplication table to the standard output. In Listing 7.19, we refactored the function so
that it returns the multiplication table as a string. Since the function no
longer prints, we renamed it multiTable. As mentioned previously, one
advantage of side-effect-free functions is they are easier to unit test. To
test printMultiTable, you would need to somehow redefine print and
println so you could check the output for correctness. You could test
multiTable more easily by checking its string result.
The other telltale sign of the imperative style in printMultiTable is its
while loop and vars. By contrast, the multiTable function uses vals, for
expressions, helper functions, and calls to mkString.
We factored out the two helper functions, makeRow and makeRowSeq, to
make the code easier to read. Function makeRowSeq uses a for expression
whose generator iterates through column numbers 1 through 10. The body of
this for calculates the product of row and column, determines the padding
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Section 7.8
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// Returns a row as a sequence
def makeRowSeq(row: Int) =
for (col <- 1 to 10) yield {
val prod = (row * col).toString
val padding = " " * (4 - prod.length)
padding + prod
}
// Returns a row as a string
def makeRow(row: Int) = makeRowSeq(row).mkString
// Returns table as a string with one row per line
def multiTable() = {
val tableSeq = // a sequence of row strings
for (row <- 1 to 10)
yield makeRow(row)
tableSeq.mkString("\n")
}
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Listing 7.19 · A functional way to create a multiplication table.
needed for the product, and yields the result of concatenating the padding
and product strings. The result of the for expression will be a sequence
(some subclass of scala.Seq) containing these yielded strings as elements.
The other helper function, makeRow, simply invokes mkString on the result returned by makeRowSeq. mkString will concatenate the strings in the
sequence and return them as one string.
The multiTable method first initializes tableSeq with the result of a
for expression whose generator iterates through row numbers 1 to 10, and
for each calls makeRow to get the string for that row. This string is yielded;
thus the result of this for expression will be a sequence of row strings. The
only remaining task is to convert the sequence of strings into a single string.
The call to mkString accomplishes this, and because we pass "\n", we get
an end of line character inserted between each string. If you pass the string
returned by multiTable to println, you’ll see the same output that’s produced by calling printMultiTable.
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Section 7.9
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1
2
3
4
5
6
7
8
9
10
7.9
2
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20
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18
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30
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36
40
5
10
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25
30
35
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45
50
6
12
18
24
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54
60
7
14
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70
8
16
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64
72
80
9 10
18 20
27 30
36 40
45 50
54 60
63 70
72 80
81 90
90 100
Conclusion
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Scala’s built-in control structures are minimal, but they do the job. They
act much like their imperative equivalents, but because they tend to result
in a value, they support a functional style, too. Just as important, they are
careful in what they omit, thus leaving room for one of Scala’s most powerful
features, the function literal, which will be described in the next chapter.
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179
Chapter 8
Functions and Closures
When programs get larger, you need some way to divide them into smaller,
more manageable pieces. For dividing up control flow, Scala offers an approach familiar to all experienced programmers: divide the code into functions. In fact, Scala offers several ways to define functions that are not
present in Java. Besides methods, which are functions that are members
of some object, there are also functions nested within functions, function literals, and function values. This chapter takes you on a tour through all of
these flavors of functions in Scala.
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8.1
Methods
The most common way to define a function is as a member of some object;
such a function is called a method. As an example, Listing 8.1 shows two
methods that together read a file with a given name and print out all lines
whose length exceeds a given width. Every printed line is prefixed with the
name of the file it appears in.
The processFile method takes a filename and width as parameters.
It creates a Source object from the file name and, in the generator of the
for expression, calls getLines on the source. As mentioned in Step 12 of
Chapter 3, getLines returns an iterator that provides one line from the file
on each iteration, excluding the end-of-line character. The for expression
processes each of these lines by calling the helper method, processLine.
The processLine method takes three parameters: a filename, a width,
and a line. It tests whether the length of the line is greater than the given
width, and, if so, it prints the filename, a colon, and the line.
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Section 8.1
Chapter 8 · Functions and Closures
import scala.io.Source
object LongLines {
def processFile(filename: String, width: Int) = {
val source = Source.fromFile(filename)
for (line <- source.getLines())
processLine(filename, width, line)
}
private def processLine(filename: String,
width: Int, line: String) = {
if (line.length > width)
println(filename + ": " + line.trim)
}
}
Listing 8.1 · LongLines with a private processLine method.
To use LongLines from the command line, we’ll create an application
that expects the line width as the first command-line argument, and interprets
subsequent arguments as filenames:1
object FindLongLines {
def main(args: Array[String]) = {
val width = args(0).toInt
for (arg <- args.drop(1))
LongLines.processFile(arg, width)
}
}
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Here’s how you’d use this application to find the lines in LongLines.scala
that are over 45 characters in length (there’s just one):
$ scala FindLongLines 45 LongLines.scala
LongLines.scala: def processFile(filename: String, width: Int) = {
1 In this book, we usually won’t check command-line arguments for validity in example
applications, both to save trees and reduce boilerplate code that can obscure the example’s
important code. The trade-off is that instead of producing a helpful error message when given
bad input, our example applications will throw an exception.
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Section 8.2
Chapter 8 · Functions and Closures
So far, this is very similar to what you would do in any object-oriented
language. However, the concept of a function in Scala is more general than
a method. Scala’s other ways to express functions will be explained in the
following sections.
8.2
Local functions
The construction of the processFile method in the previous section demonstrated an important design principle of the functional programming style:
programs should be decomposed into many small functions that each do a
well-defined task. Individual functions are often quite small. The advantage
of this style is that it gives a programmer many building blocks that can be
flexibly composed to do more difficult things. Each building block should be
simple enough to be understood individually.
One problem with this approach is that all the helper function names can
pollute the program namespace. In the interpreter this is not so much of a
problem, but once functions are packaged in reusable classes and objects, it’s
desirable to hide the helper functions from clients of a class. They often do
not make sense individually, and you often want to keep enough flexibility
to delete the helper functions if you later rewrite the class a different way.
In Java, your main tool for this purpose is the private method. This
private-method approach works in Scala as well, as demonstrated in Listing 8.1, but Scala offers an additional approach: you can define functions
inside other functions. Just like local variables, such local functions are visible only in their enclosing block. Here’s an example:
def processFile(filename: String, width: Int) = {
def processLine(filename: String,
width: Int, line: String) = {
if (line.length > width)
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println(filename + ": " + line.trim)
}
val source = Source.fromFile(filename)
for (line <- source.getLines()) {
processLine(filename, width, line)
}
}
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Section 8.2
Chapter 8 · Functions and Closures
In this example, we refactored the original LongLines version, shown in
Listing 8.1, by transforming private method, processLine, into a local function of processFile. To do so we removed the private modifier, which can
only be applied (and is only needed) for members, and placed the definition
of processLine inside the definition of processFile. As a local function,
processLine is in scope inside processFile, but inaccessible outside.
Now that processLine is defined inside processFile, however, another improvement becomes possible. Notice how filename and width are
passed unchanged into the helper function? This is not necessary because
local functions can access the parameters of their enclosing function. You
can just use the parameters of the outer processLine function, as shown in
Listing 8.2.
import scala.io.Source
object LongLines {
def processFile(filename: String, width: Int) = {
def processLine(line: String) = {
if (line.length > width)
println(filename + ": " + line.trim)
}
val source = Source.fromFile(filename)
for (line <- source.getLines())
processLine(line)
}
}
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Listing 8.2 · LongLines with a local processLine function.
Simpler, isn’t it? This use of an enclosing function’s parameters is a
common and useful example of the general nesting Scala provides. The
nesting and scoping described in Section 7.7 applies to all Scala constructs,
including functions. It’s a simple principle, but very powerful, especially in
a language with first-class functions.
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183
Section 8.3
8.3
Chapter 8 · Functions and Closures
First-class functions
Scala has first-class functions. Not only can you define functions and call
them, but you can write down functions as unnamed literals and then pass
them around as values. We introduced function literals in Chapter 2 and
showed the basic syntax in Figure 2.2 on page 75.
A function literal is compiled into a class that when instantiated at runtime is a function value.2 Thus the distinction between function literals and
values is that function literals exist in the source code, whereas function values exist as objects at runtime. The distinction is much like that between
classes (source code) and objects (runtime).
Here is a simple example of a function literal that adds one to a number:
(x: Int) => x + 1
The => designates that this function converts the thing on the left (any integer
x) to the thing on the right (x + 1). So, this is a function mapping any integer
x to x + 1.
Function values are objects, so you can store them in variables if you like.
They are functions, too, so you can invoke them using the usual parentheses
function-call notation. Here is an example of both activities:
scala> var increase = (x: Int) => x + 1
increase: Int => Int =
scala> increase(10)
res0: Int = 11
Because increase, in this example, is a var, you can assign a different
function value to it later on.
scala> increase = (x: Int) => x + 9999
increase: Int => Int =
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scala> increase(10)
res1: Int = 10009
2 Every function value is an instance of some class that extends one of several FunctionN
traits in package scala, such as Function0 for functions with no parameters, Function1 for
functions with one parameter, and so on. Each FunctionN trait has an apply method used to
invoke the function.
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Section 8.3
Chapter 8 · Functions and Closures
If you want to have more than one statement in the function literal, surround
its body by curly braces and put one statement per line, thus forming a block.
Just like a method, when the function value is invoked, all of the statements
will be executed, and the value returned from the function is whatever results
from evaluating the last expression.
scala> increase = (x: Int) => {
println("We")
println("are")
println("here!")
x + 1
}
increase: Int => Int =
scala> increase(10)
We
are
here!
res2: Int = 11
So now you have seen the nuts and bolts of function literals and function values. Many Scala libraries give you opportunities to use them. For example,
a foreach method is available for all collections.3 It takes a function as an
argument and invokes that function on each of its elements. Here is how it
can be used to print out all of the elements of a list:
scala> val someNumbers = List(-11, -10, -5, 0, 5, 10)
someNumbers: List[Int] = List(-11, -10, -5, 0, 5, 10)
scala> someNumbers.foreach((x: Int) => println(x))
-11
-10
-5
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0
5
10
3 A foreach
method is defined in trait Traversable, a common supertrait of List, Set,
Array, and Map. See Chapter 17 for the details.
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Section 8.4
Chapter 8 · Functions and Closures
As another example, collection types also have a filter method. This
method selects those elements of a collection that pass a test the user supplies. That test is supplied using a function. For example, the function
(x: Int) => x > 0 could be used for filtering. This function maps positive
integers to true and all others to false. Here is how to use it with filter:
scala> someNumbers.filter((x: Int) => x > 0)
res4: List[Int] = List(5, 10)
Methods like foreach and filter are described further later in the book.
Chapter 16 talks about their use in class List. Chapter 17 discusses their use
with other collection types.
8.4
Short forms of function literals
Scala provides a number of ways to leave out redundant information and
write function literals more briefly. Keep your eyes open for these opportunities, because they allow you to remove clutter from your code.
One way to make a function literal more brief is to leave off the parameter
types. Thus, the previous example with filter could be written like this:
scala> someNumbers.filter((x) => x > 0)
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res5: List[Int] = List(5, 10)
The Scala compiler knows that x must be an integer, because it sees that you
are immediately using the function to filter a list of integers (referred to by
someNumbers). This is called target typing because the targeted usage of an
expression (in this case, an argument to someNumbers.filter()) is allowed
to influence the typing of that expression (in this case to determine the type
of the x parameter). The precise details of target typing are not important.
You can simply start by writing a function literal without the argument type,
and if the compiler gets confused, add in the type. Over time you’ll get a feel
for which situations the compiler can and cannot puzzle out.
A second way to remove useless characters is to leave out parentheses
around a parameter whose type is inferred. In the previous example, the
parentheses around x are unnecessary:
scala> someNumbers.filter(x => x > 0)
res6: List[Int] = List(5, 10)
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Section 8.5
8.5
Chapter 8 · Functions and Closures
Placeholder syntax
To make a function literal even more concise, you can use underscores as
placeholders for one or more parameters, so long as each parameter appears
only one time within the function literal. For example, _ > 0 is very short
notation for a function that checks whether a value is greater than zero:
scala> someNumbers.filter(_ > 0)
res7: List[Int] = List(5, 10)
You can think of the underscore as a “blank” in the expression that needs
to be “filled in.” This blank will be filled in with an argument to the function
each time the function is invoked. For example, given that someNumbers
was initialized on page 185 to the value List(-11, -10, -5, 0, 5, 10), the
filter method will replace the blank in _ > 0 first with -11, as in -11 > 0,
then with -10, as in -10 > 0, then with -5, as in -5 > 0, and so on to the end
of the List. The function literal _ > 0, therefore, is equivalent to the slightly
more verbose x => x > 0, as demonstrated here:
scala> someNumbers.filter(x => x > 0)
res8: List[Int] = List(5, 10)
Sometimes when you use underscores as placeholders for parameters,
the compiler might not have enough information to infer missing parameter
types. For example, suppose you write _ + _ by itself:
scala> val f = _ + _
:7: error: missing parameter type for expanded
function ((x$1, x$2) => x$1.$plus(x$2))
val f = _ + _
ˆ
In such cases, you can specify the types using a colon, like this:
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scala> val f = (_: Int) + (_: Int)
f: (Int, Int) => Int =
scala> f(5, 10)
res9: Int = 15
Note that _ + _ expands into a literal for a function that takes two parameters. This is why you can use this short form only if each parameter appears
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187
Section 8.6
Chapter 8 · Functions and Closures
in the function literal exactly once. Multiple underscores mean multiple parameters, not reuse of a single parameter repeatedly. The first underscore
represents the first parameter, the second underscore the second parameter,
the third underscore the third parameter, and so on.
8.6
Partially applied functions
Although the previous examples substitute underscores in place of individual
parameters, you can also replace an entire parameter list with an underscore.
For example, rather than writing println(_), you could write println _.
Here’s an example:
someNumbers.foreach(println _)
Scala treats this short form exactly as if you had written the following:
someNumbers.foreach(x => println(x))
Thus, the underscore in this case is not a placeholder for a single parameter.
It is a placeholder for an entire parameter list. Remember that you need
to leave a space between the function name and the underscore; otherwise,
the compiler will think you are referring to a different symbol, such as, for
example, a method named println_, which likely does not exist.
When you use an underscore in this way, you are writing a partially applied function. In Scala, when you invoke a function, passing in any needed
arguments, you apply that function to the arguments. For example, given the
following function:
scala> def sum(a: Int, b: Int, c: Int) = a + b + c
sum: (a: Int, b: Int, c: Int)Int
You could apply the function sum to the arguments 1, 2, and 3 like this:
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scala> sum(1, 2, 3)
res10: Int = 6
A partially applied function is an expression in which you don’t supply all
of the arguments needed by the function. Instead, you supply some, or none,
of the needed arguments. For example, to create a partially applied function
expression involving sum, in which you supply none of the three required
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Section 8.6
Chapter 8 · Functions and Closures
arguments, you just place an underscore after “sum”. The resulting function
can then be stored in a variable. Here’s an example:
scala> val a = sum _
a: (Int, Int, Int) => Int =
Given this code, the Scala compiler instantiates a function value that takes
the three integer parameters missing from the partially applied function expression, sum _, and assigns a reference to that new function value to the
variable a. When you apply three arguments to this new function value, it
will turn around and invoke sum, passing in those same three arguments:
scala> a(1, 2, 3)
res11: Int = 6
Here’s what just happened: The variable named a refers to a function value
object. This function value is an instance of a class generated automatically
by the Scala compiler from sum _, the partially applied function expression.
The class generated by the compiler has an apply method that takes three
arguments.4 The generated class’s apply method takes three arguments because three is the number of arguments missing in the sum _ expression. The
Scala compiler translates the expression a(1, 2, 3) into an invocation of the
function value’s apply method, passing in the three arguments 1, 2, and 3.
Thus, a(1, 2, 3) is a short form for:
scala> a.apply(1, 2, 3)
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res12: Int = 6
This apply method, defined in the class generated automatically by the
Scala compiler from the expression sum _, simply forwards those three missing parameters to sum, and returns the result. In this case apply invokes
sum(1, 2, 3), and returns what sum returns, which is 6.
Another way to think about this kind of expression, in which an underscore is used to represent an entire parameter list, is as a way to transform a
def into a function value. For example, if you have a local function, such as
sum(a: Int, b: Int, c: Int): Int, you can “wrap” it in a function value
whose apply method has the same parameter list and result types. When
you apply this function value to some arguments, it in turn applies sum to
4 The generated class extends trait Function3, which declares a three-arg apply method.
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those same arguments and returns the result. Although you can’t assign a
method or nested function to a variable, or pass it as an argument to another
function, you can do these things if you wrap the method or nested function
in a function value by placing an underscore after its name.
Now, although sum _ is indeed a partially applied function, it may not
be obvious to you why it is called this. It has this name because you are not
applying that function to all of its arguments. In the case of sum _, you are
applying it to none of its arguments. But you can also express a partially
applied function by supplying only some of the required arguments. Here’s
an example:
scala> val b = sum(1, _: Int, 3)
b: Int => Int =
In this case, you’ve supplied the first and last argument to sum, but not
the middle argument. Since only one argument is missing, the Scala compiler generates a new function class whose apply method takes one argument. When invoked with that one argument, this generated function’s
apply method invokes sum, passing in 1, the argument passed to the function, and 3. Here are some examples:
scala> b(2)
res13: Int = 6
In this case, b.apply invoked sum(1, 2, 3).
scala> b(5)
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res14: Int = 9
And in this case, b.apply invoked sum(1, 5, 3).
If you are writing a partially applied function expression in which you
leave off all parameters, such as println _ or sum _, you can express it
more concisely by leaving off the underscore if a function is required at that
point in the code. For example, instead of printing out each of the numbers
in someNumbers (defined on page 185) like this:
someNumbers.foreach(println _)
You could just write:
someNumbers.foreach(println)
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This last form is allowed only in places where a function is required, such as
the invocation of foreach in this example. The compiler knows a function
is required in this case, because foreach requires that a function be passed
as an argument. In situations where a function is not required, attempting to
use this form will cause a compilation error. Here’s an example:
scala> val c = sum
:8: error: missing arguments for method sum;
follow this method with `_' if you want to treat it as a
partially applied function
val c = sum
ˆ
scala> val d = sum _
d: (Int, Int, Int) => Int =
scala> d(10, 20, 30)
res14: Int = 60
8.7
Closures
So far in this chapter, all the examples of function literals have referred only
to passed parameters. For example, in (x: Int) => x > 0, the only variable
used in the function body, x > 0, is x, which is defined as a parameter to the
function. You can, however, refer to variables defined elsewhere:
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(x: Int) => x + more
// how much more?
This function adds “more” to its argument, but what is more? From the point
of view of this function, more is a free variable because the function literal
does not itself give a meaning to it. The x variable, by contrast, is a bound
variable because it does have a meaning in the context of the function: it
is defined as the function’s lone parameter, an Int. If you try using this
function literal by itself, without any more defined in its scope, the compiler
will complain:
scala> (x: Int) => x + more
:8: error: not found: value more
(x: Int) => x + more
ˆ
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Why the trailing underscore?
Scala’s syntax for partially applied functions highlights a difference
in the design trade-offs of Scala and classical functional languages,
such as Haskell or ML. In these languages, partially applied functions
are considered the normal case. Furthermore, these languages have
a fairly strict static type system that will usually highlight every error
with partial applications that you can make. Scala bears a much closer
relation to imperative languages, such as Java, where a method that’s
not applied to all its arguments is considered an error. Furthermore,
the object-oriented tradition of subtyping and a universal root type
accepts some programs that would be considered erroneous in classical
functional languages.
For instance, say you mistook the drop(n: Int) method of List
for tail(), and therefore forgot you need to pass a number to drop.
You might write, “println(drop)”. Had Scala adopted the classical
functional tradition that partially applied functions are OK everywhere,
this code would type check. However, you might be surprised to find
out that the output printed by this println statement would always be
! What would have happened is that the expression drop
would have been treated as a function object. Because println takes
objects of any type, this would have compiled OK, but it would have
given an unexpected result.
To avoid situations like this, Scala normally requires you to specify
function arguments that are left out explicitly, even if the indication is
as simple as a ‘_’. Scala allows you to leave off even the _ only when a
function type is expected.
On the other hand, the same function literal will work fine so long as
there is something available named more:
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scala> var more = 1
more: Int = 1
scala> val addMore = (x: Int) => x + more
addMore: Int => Int =
scala> addMore(10)
res16: Int = 11
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The function value (the object) that’s created at runtime from this function
literal is called a closure. The name arises from the act of “closing” the function literal by “capturing” the bindings of its free variables. A function literal
with no free variables, such as (x: Int) => x + 1, is called a closed term,
where a term is a bit of source code. Thus a function value created at runtime from this function literal is not a closure in the strictest sense, because
(x: Int) => x + 1 is already closed as written. But any function literal with
free variables, such as (x: Int) => x + more, is an open term. Therefore,
any function value created at runtime from (x: Int) => x + more will, by
definition, require that a binding for its free variable, more, be captured. The
resulting function value, which will contain a reference to the captured more
variable, is called a closure because the function value is the end product of
the act of closing the open term, (x: Int) => x + more.
This example brings up a question: What happens if more changes after the closure is created? In Scala, the answer is that the closure sees the
change. For example:
scala> more = 9999
more: Int = 9999
scala> addMore(10)
res17: Int = 10009
Intuitively, Scala’s closures capture variables themselves, not the value to
which variables refer.5 As the previous example shows, the closure created
for (x: Int) => x + more sees the change to more made outside the closure.
The same is true in the opposite direction. Changes made by a closure to a
captured variable are visible outside the closure. Here’s an example:
scala> val someNumbers = List(-11, -10, -5, 0, 5, 10)
someNumbers: List[Int] = List(-11, -10, -5, 0, 5, 10)
scala> var sum = 0
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sum: Int = 0
scala> someNumbers.foreach(sum +=
_)
5 By
contrast, Java’s inner classes do not allow you to access modifiable variables in
surrounding scopes at all, so there is no difference between capturing a variable and capturing
its currently held value.
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scala> sum
res19: Int = -11
This example uses a roundabout way to sum the numbers in a List. Variable
sum is in a surrounding scope from the function literal sum += _, which adds
numbers to sum. Even though it is the closure modifying sum at runtime, the
resulting total, -11, is still visible outside the closure.
What if a closure accesses some variable that has several different copies
as the program runs? For example, what if a closure uses a local variable of
some function, and the function is invoked many times? Which instance of
that variable gets used at each access?
Only one answer is consistent with the rest of the language: the instance
used is the one that was active at the time the closure was created. For
example, here is a function that creates and returns “increase” closures:
def makeIncreaser(more: Int) = (x: Int) => x + more
Each time this function is called it will create a new closure. Each closure
will access the more variable that was active when the closure was created.
scala> val inc1 = makeIncreaser(1)
inc1: Int => Int =
scala> val inc9999 = makeIncreaser(9999)
inc9999: Int => Int =
When you call makeIncreaser(1), a closure is created and returned that
captures the value 1 as the binding for more. Similarly, when you call
makeIncreaser(9999), a closure that captures the value 9999 for more is
returned. When you apply these closures to arguments (in this case, there’s
just one argument, x, which must be passed in), the result that comes back
depends on how more was defined when the closure was created:
scala> inc1(10)
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res20: Int = 11
scala> inc9999(10)
res21: Int = 10009
It makes no difference that the more in this case is a parameter to a method
call that has already returned. The Scala compiler rearranges things in cases
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like these so that the captured parameter lives out on the heap, instead of the
stack, and thus can outlive the method call that created it. This rearrangement
is all taken care of automatically, so you don’t have to worry about it. Capture
any variable you like: val, var, or parameter.
8.8
Special function call forms
Most functions and function calls you encounter will be as you have seen
so far in this chapter. The function will have a fixed number of parameters,
the call will have an equal number of arguments, and the arguments will be
specified in the same order and number as the parameters.
Since function calls are so central to programming in Scala, however, a
few special forms of function definitions and function calls have been added
to the language to address some special needs. Scala supports repeated parameters, named arguments, and default arguments.
Repeated parameters
Scala allows you to indicate that the last parameter to a function may be
repeated. This allows clients to pass variable length argument lists to the
function. To denote a repeated parameter, place an asterisk after the type of
the parameter. For example:
scala> def echo(args: String*) =
for (arg <- args) println(arg)
echo: (args: String*)Unit
Defined this way, echo can be called with zero to many String arguments:
scala> echo()
scala> echo("one")
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one
scala> echo("hello", "world!")
hello
world!
Inside the function, the type of the repeated parameter is an Array of
the declared type of the parameter. Thus, the type of args inside the echo
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function, which is declared as type “String*” is actually Array[String].
Nevertheless, if you have an array of the appropriate type, and you attempt
to pass it as a repeated parameter, you’ll get a compiler error:
scala> val arr = Array("What's", "up", "doc?")
arr: Array[String] = Array(What's, up, doc?)
scala> echo(arr)
:10: error: type mismatch;
found
: Array[String]
required: String
echo(arr)
ˆ
To accomplish this, you’ll need to append the array argument with a colon
and an _* symbol, like this:
scala> echo(arr: _*)
What's
up
doc?
This notation tells the compiler to pass each element of arr as its own argument to echo, rather than all of it as a single argument.
Named arguments
In a normal function call, the arguments in the call are matched one by one
in the order of the parameters of the called function:
scala> def speed(distance: Float, time: Float): Float =
distance / time
speed: (distance: Float, time: Float)Float
scala> speed(100, 10)
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res27: Float = 10.0
In this call, the 100 is matched to distance and the 10 to time. The 100
and 10 are matched in the same order as the formal parameters are listed.
Named arguments allow you to pass arguments to a function in a different order. The syntax is simply that each argument is preceded by a parameter name and an equals sign. For example, the following call to speed is
equivalent to speed(100,10):
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scala> speed(distance = 100, time = 10)
res28: Float = 10.0
Called with named arguments, the arguments can be reversed without
changing the meaning:
scala> speed(time = 10, distance = 100)
res29: Float = 10.0
It is also possible to mix positional and named arguments. In that case, the
positional arguments come first. Named arguments are most frequently used
in combination with default parameter values.
Default parameter values
Scala lets you specify default values for function parameters. The argument
for such a parameter can optionally be omitted from a function call, in which
case the corresponding argument will be filled in with the default.
An example is shown in Listing 8.3. Function printTime has one parameter, out, and it has a default value of Console.out.
def printTime(out: java.io.PrintStream = Console.out) =
out.println("time = " + System.currentTimeMillis())
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Listing 8.3 · A parameter with a default value.
If you call the function as printTime(), thus specifying no argument to
be used for out, then out will be set to its default value of Console.out.
You could also call the function with an explicit output stream. For example,
you could send logging to the standard error output by calling the function
as printTime(Console.err).
Default parameters are especially helpful when used in combination with
named parameters. In Listing 8.4, function printTime2 has two optional
parameters. The out parameter has a default of Console.out, and the
divisor parameter has a default value of 1.
Function printTime2 can be called as printTime2() to have both parameters filled in with their default values. Using named arguments, however, either one of the parameters can be specified while leaving the other as
the default. To specify the output stream, call it like this:
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def printTime2(out: java.io.PrintStream = Console.out,
divisor: Int = 1) =
out.println("time = " + System.currentTimeMillis()/divisor)
Listing 8.4 · A function with two parameters that have defaults.
printTime2(out = Console.err)
To specify the time divisor, call it like this:
printTime2(divisor = 1000)
8.9
Tail recursion
In Section 7.2, we mentioned that to transform a while loop that updates
vars into a more functional style that uses only vals, you may sometimes
need to use recursion. Here’s an example of a recursive function that approximates a value by repeatedly improving a guess until it is good enough:
def approximate(guess: Double): Double =
if (isGoodEnough(guess)) guess
else approximate(improve(guess))
A function like this is often used in search problems, with appropriate implementations for isGoodEnough and improve. If you want the approximate
function to run faster, you might be tempted to write it with a while loop to
try and speed it up, like this:
def approximateLoop(initialGuess: Double): Double = {
var guess = initialGuess
while (!isGoodEnough(guess))
guess = improve(guess)
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guess
}
Which of the two versions of approximate is preferable? In terms of brevity
and var avoidance, the first, functional one wins. But is the imperative approach perhaps more efficient? In fact, if we measure execution times, it
turns out that they are almost exactly the same!
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This might seem surprising because a recursive call looks much more
“expansive” than a simple jump from the end of a loop to its beginning.
However, in the case of approximate above, the Scala compiler is able to
apply an important optimization. Note that the recursive call is the last thing
that happens in the evaluation of function approximate’s body. Functions
like approximate, which call themselves as their last action, are called tail
recursive. The Scala compiler detects tail recursion and replaces it with a
jump back to the beginning of the function, after updating the function parameters with the new values.
The moral is that you should not shy away from using recursive algorithms to solve your problem. Often, a recursive solution is more elegant
and concise than a loop-based one. If the solution is tail recursive, there
won’t be any runtime overhead to be paid.
Tracing tail-recursive functions
A tail-recursive function will not build a new stack frame for each call; all
calls will execute in a single frame. This may surprise a programmer inspecting a stack trace of a program that failed. For example, this function calls
itself some number of times then throws an exception:
def boom(x: Int): Int =
if (x == 0) throw new Exception("boom!")
else boom(x - 1) + 1
This function is not tail recursive, because it performs an increment operation
after the recursive call. You’ll get what you expect when you run it:
scala>
boom(3)
java.lang.Exception: boom!
at .boom(:5)
at .boom(:6)
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at .boom(:6)
at .boom(:6)
at .(:6)
...
If you now modify boom so that it does become tail recursive:
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Tail call optimization
The compiled code for approximate is essentially the same as the
compiled code for approximateLoop. Both functions compile down
to the same thirteen instructions of Java bytecodes. If you look through
the bytecodes generated by the Scala compiler for the tail recursive
method, approximate, you’ll see that although both isGoodEnough
and improve are invoked in the body of the method, approximate is
not. The Scala compiler optimized away the recursive call:
public double approximate(double);
Code:
0:
aload_0
1:
astore_3
2:
aload_0
3:
dload_1
4:
invokevirtual
7:
ifeq
10:
dload_1
11:
dreturn
12:
aload_0
13:
dload_1
14:
invokevirtual
17:
dstore_1
18:
goto
#24; //Method isGoodEnough:(D)Z
12
#27; //Method improve:(D)D
2
def bang(x: Int): Int =
if (x == 0) throw new Exception("bang!")
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else bang(x - 1)
You’ll get:
scala> bang(5)
java.lang.Exception: bang!
at .bang(:5)
at .(:6) ...
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This time, you see only a single stack frame for bang. You might think that
bang crashed before it called itself, but this is not the case. If you think you
might be confused by tail-call optimizations when looking at a stack trace,
you can turn them off by giving the following argument to the scala shell
or to the scalac compiler:
-g:notailcalls
With that option specified, you will get a longer stack trace:
scala> bang(5)
java.lang.Exception: bang!
at .bang(:5)
at .bang(:5)
at .bang(:5)
at .bang(:5)
at .bang(:5)
at .bang(:5)
at .(:6) ...
Limits of tail recursion
The use of tail recursion in Scala is fairly limited because the JVM instruction set makes implementing more advanced forms of tail recursion very
difficult. Scala only optimizes directly recursive calls back to the same function making the call. If the recursion is indirect, as in the following example
of two mutually recursive functions, no optimization is possible:
def isEven(x: Int): Boolean =
if (x == 0) true else isOdd(x - 1)
def isOdd(x: Int): Boolean =
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if (x == 0) false else isEven(x - 1)
You also won’t get a tail-call optimization if the final call goes to a function
value. Consider for instance the following recursive code:
val funValue = nestedFun _
def nestedFun(x: Int) : Unit = {
if (x != 0) { println(x); funValue(x - 1) }
}
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The funValue variable refers to a function value that essentially wraps a call
to nestedFun. When you apply the function value to an argument, it turns
around and applies nestedFun to that same argument, and returns the result.
Therefore, you might hope the Scala compiler would perform a tail-call optimization, but in this case it would not. Tail-call optimization is limited to
situations where a method or nested function calls itself directly as its last operation, without going through a function value or some other intermediary.
(If you don’t fully understand tail recursion yet, see Section 8.9).
8.10
Conclusion
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This chapter has given you a grand tour of functions in Scala. In addition
to methods, Scala provides local functions, function literals, and function
values. In addition to normal function calls, Scala provides partially applied
functions and functions with repeated parameters. When possible, function
calls are implemented as optimized tail calls, and thus many nice-looking
recursive functions run just as quickly as hand-optimized versions that use
while loops. The next chapter will build on these foundations and show how
Scala’s rich support for functions helps you abstract over control.
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202
Chapter 9
Control Abstraction
In Chapter 7, we pointed out that Scala doesn’t have many built-in control
abstractions because it gives you the ability to create your own. In the previous chapter, you learned about function values. In this chapter, we’ll show
you how to apply function values to create new control abstractions. Along
the way, you’ll also learn about currying and by-name parameters.
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9.1
Reducing code duplication
All functions are separated into common parts, which are the same in every
invocation of the function, and non-common parts, which may vary from
one function invocation to the next. The common parts are in the body of
the function, while the non-common parts must be supplied via arguments.
When you use a function value as an argument, the non-common part of
the algorithm is itself some other algorithm! At each invocation of such
a function, you can pass in a different function value as an argument, and
the invoked function will, at times of its choosing, invoke the passed function value. These higher-order functions—functions that take functions as
parameters—give you extra opportunities to condense and simplify code.
One benefit of higher-order functions is they enable you to create control
abstractions that allow you to reduce code duplication. For example, suppose
you are writing a file browser, and you want to provide an API that allows
users to search for files matching some criterion. First, you add a facility to
search for files whose names end in a particular string. This would enable
your users to find, for example, all files with a “.scala” extension. You
could provide such an API by defining a public filesEnding method inside
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Section 9.1
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a singleton object like this:
object FileMatcher {
private def filesHere = (new java.io.File(".")).listFiles
def filesEnding(query: String) =
for (file <- filesHere; if file.getName.endsWith(query))
yield file
}
The filesEnding method obtains the list of all files in the current directory using the private helper method filesHere, then filters them based on
whether each file name ends with the user-specified query. Given filesHere
is private, the filesEnding method is the only accessible method defined in
FileMatcher, the API you provide to your users.
So far so good, and there is no repeated code yet. Later on, though, you
decide to let people search based on any part of the file name. This is good for
when your users cannot remember if they named a file phb-important.doc,
stupid-phb-report.doc, may2003salesdoc.phb, or something entirely
different; they just know that “phb” appears in the name somewhere. You go
back to work and add this function to your FileMatcher API:
def filesContaining(query: String) =
for (file <- filesHere; if file.getName.contains(query))
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yield file
This function works just like filesEnding. It searches filesHere, checks
the name, and returns the file if the name matches. The only difference is
that this function uses contains instead of endsWith.
The months go by, and the program becomes more successful. Eventually, you give in to the requests of a few power users who want to search
based on regular expressions. These sloppy guys have immense directories
with thousands of files, and they would like to do things like find all “pdf”
files that have “oopsla” in the title somewhere. To support them, you write
this function:
def filesRegex(query: String) =
for (file <- filesHere; if file.getName.matches(query))
yield file
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Experienced programmers will notice all of this repetition and wonder if it
can be factored into a common helper function. Doing it the obvious way
does not work, however. You would like to be able to do the following:
def filesMatching(query: String, method ) =
for (file <- filesHere; if file.getName.method (query))
yield file
This approach would work in some dynamic languages, but Scala does not
allow pasting together code at runtime like this. So what do you do?
Function values provide an answer. While you cannot pass around a
method name as a value, you can get the same effect by passing around a
function value that calls the method for you. In this case, you could add a
matcher parameter to the method whose sole purpose is to check a file name
against a query:
def filesMatching(query: String,
matcher: (String, String) => Boolean) = {
for (file <- filesHere; if matcher(file.getName, query))
yield file
}
In this version of the method, the if clause now uses matcher to check the
file name against the query. Precisely what this check does depends on what
is specified as the matcher. Take a look, now, at the type of matcher itself.
It is a function, and thus has a => in the type. This function takes two string
arguments—the file name and the query—and returns a boolean, so the type
of this function is (String, String) => Boolean.
Given this new filesMatching helper method, you can simplify the
three searching methods by having them call the helper method, passing in
an appropriate function:
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def filesEnding(query: String) =
filesMatching(query, _.endsWith(_))
def filesContaining(query: String) =
filesMatching(query, _.contains(_))
def filesRegex(query: String) =
filesMatching(query, _.matches(_))
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The function literals shown in this example use the placeholder syntax, introduced in the previous chapter, which may not as yet feel very natural to
you. So here’s a clarification of how placeholders are used: The function
literal _.endsWith(_), used in the filesEnding method, means the same
thing as:
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(fileName: String, query: String) => fileName.endsWith(query)
Because filesMatching takes a function that requires two String arguments, you need not specify the types of the arguments; you could just write
(fileName, query) => fileName.endsWith(query). Since the parameters are each used only once in the body of the function (i.e., the first parameter, fileName, is used first in the body, and the second parameter, query,
is used second), you can use the placeholder syntax: _.endsWith(_). The
first underscore is a placeholder for the first parameter, the file name, and the
second underscore a placeholder for the second parameter, the query string.
This code is already simplified, but it can actually be even shorter. Notice that the query gets passed to filesMatching, but filesMatching does
nothing with the query except to pass it back to the passed matcher function. This passing back and forth is unnecessary because the caller already
knew the query to begin with! You might as well remove the query parameter from filesMatching and matcher, thus simplifying the code as shown
in Listing 9.1.
This example demonstrates the way in which first-class functions can
help you eliminate code duplication where it would be very difficult to do
so without them. In Java, for example, you could create an interface containing a method that takes one String and returns a Boolean, then create
and pass anonymous inner class instances that implement this interface to
filesMatching. Although this approach would remove the code duplication you are trying to eliminate, it would, at the same time, add as much or
more new code. Thus the benefit is not worth the cost, and you may as well
live with the duplication.
Moreover, this example demonstrates how closures can help you reduce
code duplication. The function literals used in the previous example, such as
_.endsWith(_) and _.contains(_), are instantiated at runtime into function values that are not closures because they don’t capture any free variables. Both variables used in the expression, _.endsWith(_), for example,
are represented by underscores, which means they are taken from arguments
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object FileMatcher {
private def filesHere = (new java.io.File(".")).listFiles
private def filesMatching(matcher: String => Boolean) =
for (file <- filesHere; if matcher(file.getName))
yield file
def filesEnding(query: String) =
filesMatching(_.endsWith(query))
def filesContaining(query: String) =
filesMatching(_.contains(query))
def filesRegex(query: String) =
filesMatching(_.matches(query))
}
Listing 9.1 · Using closures to reduce code duplication.
to the function. Thus, _.endsWith(_) uses two bound variables, and no free
variables. By contrast, the function literal _.endsWith(query), used in the
most recent example, contains one bound variable, the argument represented
by the underscore, and one free variable named query. It is only because
Scala supports closures that you were able to remove the query parameter
from filesMatching in the most recent example, thereby simplifying the
code even further.
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9.2
Simplifying client code
The previous example demonstrated that higher-order functions can help reduce code duplication as you implement an API. Another important use of
higher-order functions is to put them in an API itself to make client code
more concise. A good example is provided by the special-purpose looping
methods of Scala’s collection types.1 Many of these are listed in Table 3.1
in Chapter 3, but take a look at just one example for now to see why these
methods are so useful.
1 These special-purpose looping methods are defined in trait Traversable, which is extended by List, Set, and Map. See Chapter 17 for a discussion.
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Consider exists, a method that determines whether a passed value is
contained in a collection. You could, of course, search for an element by
having a var initialized to false, looping through the collection checking
each item, and setting the var to true if you find what you are looking for.
Here’s a method that uses this approach to determine whether a passed List
contains a negative number:
def containsNeg(nums: List[Int]): Boolean = {
var exists = false
for (num <- nums)
if (num < 0)
exists = true
exists
}
If you define this method in the interpreter, you can call it like this:
scala> containsNeg(List(1, 2, 3, 4))
res0: Boolean = false
scala> containsNeg(List(1, 2, -3, 4))
res1: Boolean = true
A more concise way to define the method, though, is by calling the higherorder function exists on the passed List, like this:
def containsNeg(nums: List[Int]) = nums.exists(_ < 0)
This version of containsNeg yields the same results as the previous:
scala> containsNeg(Nil)
res2: Boolean = false
scala> containsNeg(List(0, -1, -2))
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res3: Boolean = true
The exists method represents a control abstraction. It is a special-purpose
looping construct provided by the Scala library, rather than built into the
Scala language like while or for. In the previous section, the higher-order
function, filesMatching, reduces code duplication in the implementation
of the object FileMatcher. The exists method provides a similar benefit,
but because exists is public in Scala’s collections API, the code duplication
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it reduces is client code of that API. If exists didn’t exist, and you wanted
to write a containsOdd method to test whether a list contains odd numbers,
you might write it like this:
def containsOdd(nums: List[Int]): Boolean = {
var exists = false
for (num <- nums)
if (num % 2 == 1)
exists = true
exists
}
If you compare the body of containsNeg with that of containsOdd, you’ll
find that everything is repeated except the test condition of an if expression.
Using exists, you could write this instead:
def containsOdd(nums: List[Int]) = nums.exists(_ % 2 == 1)
The body of the code in this version is again identical to the body of the corresponding containsNeg method (the version that uses exists), except the
condition for which to search is different. Yet the amount of code duplication
is much smaller because all of the looping infrastructure is factored out into
the exists method itself.
There are many other looping methods in Scala’s standard library. As
with exists, they can often shorten your code if you recognize opportunities
to use them.
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9.3
Currying
In Chapter 1, we said that Scala allows you to create new control abstractions that “feel like native language support.” Although the examples you’ve
seen so far are indeed control abstractions, it is unlikely anyone would mistake them for native language support. To understand how to make control
abstractions that feel more like language extensions, you first need to understand the functional programming technique called currying.
A curried function is applied to multiple argument lists, instead of just
one. Listing 9.2 shows a regular, non-curried function, which adds two Int
parameters, x and y.
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scala> def plainOldSum(x: Int, y: Int) = x + y
plainOldSum: (x: Int, y: Int)Int
scala> plainOldSum(1, 2)
res4: Int = 3
Listing 9.2 · Defining and invoking a “plain old” function.
By contrast, Listing 9.3 shows a similar function that’s curried. Instead
of one list of two Int parameters, you apply this function to two lists of one
Int parameter each.
scala> def curriedSum(x: Int)(y: Int) = x + y
curriedSum: (x: Int)(y: Int)Int
scala> curriedSum(1)(2)
res5: Int = 3
Listing 9.3 · Defining and invoking a curried function.
What’s happening here is that when you invoke curriedSum, you actually get two traditional function invocations back to back. The first function
invocation takes a single Int parameter named x, and returns a function
value for the second function. This second function takes the Int parameter
y. Here’s a function named first that does in spirit what the first traditional
function invocation of curriedSum would do:
scala> def first(x: Int) = (y: Int) => x + y
first: (x: Int)Int => Int
Applying the first function to 1—in other words, invoking the first function
and passing in 1—yields the second function:
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scala> val second = first(1)
second: Int => Int =
Applying the second function to 2 yields the result:
scala> second(2)
res6: Int = 3
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These first and second functions are just an illustration of the currying
process. They are not directly connected to the curriedSum function. Nevertheless, there is a way to get an actual reference to curriedSum’s “second”
function. You can use the placeholder notation to use curriedSum in a partially applied function expression, like this:
scala> val onePlus = curriedSum(1)_
onePlus: Int => Int =
The underscore in curriedSum(1)_ is a placeholder for the second parameter list.2 The result is a reference to a function that, when invoked, adds one
to its sole Int argument and returns the result:
scala> onePlus(2)
res7: Int = 3
And here’s how you’d get a function that adds two to its sole Int argument:
scala> val twoPlus = curriedSum(2)_
twoPlus: Int => Int =
scala> twoPlus(2)
res8: Int = 4
9.4
Writing new control structures
In languages with first-class functions, you can effectively make new control
structures even though the syntax of the language is fixed. All you need to
do is create methods that take functions as arguments.
For example, here is the “twice” control structure, which repeats an operation two times and returns the result:
scala> def twice(op: Double => Double, x: Double) = op(op(x))
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twice: (op: Double => Double, x: Double)Double
scala> twice(_ + 1, 5)
res9: Double = 7.0
2 In the previous chapter, when the placeholder notation was used on traditional methods,
like println _, you had to leave a space between the name and the underscore. In this case
you don’t, because whereas println_ is a legal identifier in Scala, curriedSum(1)_ is not.
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The type of op in this example is Double => Double, which means it is a
function that takes one Double as an argument and returns another Double.
Any time you find a control pattern repeated in multiple parts of your
code, you should think about implementing it as a new control structure.
Earlier in the chapter you saw filesMatching, a very specialized control
pattern. Consider now a more widely used coding pattern: open a resource,
operate on it, and then close the resource. You can capture this in a control
abstraction using a method like the following:
def withPrintWriter(file: File, op: PrintWriter => Unit) = {
val writer = new PrintWriter(file)
try {
op(writer)
} finally {
writer.close()
}
}
Given such a method, you can use it like this:
withPrintWriter(
new File("date.txt"),
writer => writer.println(new java.util.Date)
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)
The advantage of using this method is that it’s withPrintWriter, not user
code, that assures the file is closed at the end. So it’s impossible to forget to close the file. This technique is called the loan pattern, because a
control-abstraction function, such as withPrintWriter, opens a resource
and “loans” it to a function. For instance, withPrintWriter in the previous example loans a PrintWriter to the function, op. When the function
completes, it signals that it no longer needs the “borrowed” resource. The
resource is then closed in a finally block, to ensure it is indeed closed, regardless of whether the function completes by returning normally or throwing an exception.
One way in which you can make the client code look a bit more like a
built-in control structure is to use curly braces instead of parentheses to surround the argument list. In any method invocation in Scala in which you’re
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passing in exactly one argument, you can opt to use curly braces to surround
the argument instead of parentheses.
For example, instead of:
scala> println("Hello, world!")
Hello, world!
You could write:
scala> println { "Hello, world!" }
Hello, world!
In the second example, you used curly braces instead of parentheses to surround the arguments to println. This curly braces technique will work,
however, only if you’re passing in one argument. Here’s an attempt at violating that rule:
scala> val g = "Hello, world!"
g: String = Hello, world!
scala> g.substring { 7, 9 }
:1: error: ';' expected but ',' found.
g.substring { 7, 9 }
ˆ
Because you are attempting to pass in two arguments to substring, you
get an error when you try to surround those arguments with curly braces.
Instead, you’ll need to use parentheses:
scala> g.substring(7, 9)
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res12: String = wo
The purpose of this ability to substitute curly braces for parentheses for
passing in one argument is to enable client programmers to write function
literals between curly braces. This can make a method call feel more like a
control abstraction. Take the withPrintWriter method defined previously
as an example. In its most recent form, withPrintWriter takes two arguments, so you can’t use curly braces. Nevertheless, because the function
passed to withPrintWriter is the last argument in the list, you can use currying to pull the first argument, the File, into a separate argument list. This
will leave the function as the lone parameter of the second argument list.
Listing 9.4 shows how you’d need to redefine withPrintWriter.
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def withPrintWriter(file: File)(op: PrintWriter => Unit) = {
val writer = new PrintWriter(file)
try {
op(writer)
} finally {
writer.close()
}
}
Listing 9.4 · Using the loan pattern to write to a file.
The new version differs from the old one only in that there are now two
parameter lists with one parameter each instead of one parameter list with
two parameters. Look between the two parameters. In the previous version
of withPrintWriter, shown on page 212, you see . . . File, op. . . . But in
this version, you see . . . File)(op. . . . Given the above definition, you can
call the method with a more pleasing syntax:
val file = new File("date.txt")
withPrintWriter(file) { writer =>
writer.println(new java.util.Date)
}
In this example, the first argument list, which contains one File argument, is
written surrounded by parentheses. The second argument list, which contains
one function argument, is surrounded by curly braces.
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9.5
By-name parameters
The withPrintWriter method shown in the previous section differs from
built-in control structures of the language, such as if and while, in that
the code between the curly braces takes an argument. The function passed
to withPrintWriter requires one argument of type PrintWriter. This
argument shows up as the “writer =>” in:
withPrintWriter(file) { writer =>
writer.println(new java.util.Date)
}
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But what if you want to implement something more like if or while, where
there is no value to pass into the code between the curly braces? To help with
such situations, Scala provides by-name parameters.
As a concrete example, suppose you want to implement an assertion construct called myAssert.3 The myAssert function will take a function value
as input and consult a flag to decide what to do. If the flag is set, myAssert
will invoke the passed function and verify that it returns true. If the flag is
turned off, myAssert will quietly do nothing at all.
Without using by-name parameters, you could write myAssert like this:
var assertionsEnabled = true
def myAssert(predicate: () => Boolean) =
if (assertionsEnabled && !predicate())
throw new AssertionError
The definition is fine, but using it is a little bit awkward:
myAssert(() => 5 > 3)
You would really prefer to leave out the empty parameter list and => symbol
in the function literal and write the code like this:
myAssert(5 > 3) // Won't work, because missing () =>
By-name parameters exist precisely so that you can do this. To make a byname parameter, you give the parameter a type starting with => instead of
() =>. For example, you could change myAssert’s predicate parameter into a by-name parameter by changing its type, “() => Boolean”, into
“=> Boolean”. Listing 9.5 shows how that would look:
def byNameAssert(predicate: => Boolean) =
if (assertionsEnabled && !predicate)
throw new AssertionError
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Listing 9.5 · Using a by-name parameter.
Now you can leave out the empty parameter in the property you want
to assert. The result is that using byNameAssert looks exactly like using a
built-in control structure:
3 You’ll
call this myAssert, not assert, because Scala provides an assert of its own,
which will be described in Section 14.1.
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byNameAssert(5 > 3)
A by-name type, in which the empty parameter list, (), is left out, is only
allowed for parameters. There is no such thing as a by-name variable or a
by-name field.
Now, you may be wondering why you couldn’t simply write myAssert
using a plain old Boolean for the type of its parameter, like this:
def boolAssert(predicate: Boolean) =
if (assertionsEnabled && !predicate)
throw new AssertionError
This formulation is also legal, of course, and the code using this version of
boolAssert would still look exactly as before:
boolAssert(5 > 3)
Nevertheless, one difference exists between these two approaches that is important to note. Because the type of boolAssert’s parameter is Boolean,
the expression inside the parentheses in boolAssert(5 > 3) is evaluated before the call to boolAssert. The expression 5 > 3 yields true, which is
passed to boolAssert. By contrast, because the type of byNameAssert’s
predicate parameter is => Boolean, the expression inside the parentheses
in byNameAssert(5 > 3) is not evaluated before the call to byNameAssert.
Instead a function value will be created whose apply method will evaluate
5 > 3, and this function value will be passed to byNameAssert.
The difference between the two approaches, therefore, is that if assertions are disabled, you’ll see any side effects that the expression inside the
parentheses may have in boolAssert, but not in byNameAssert. For example, if assertions are disabled, attempting to assert on “x / 0 == 0” will yield
an exception in boolAssert’s case:
scala> val x = 5
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x: Int = 5
scala> var assertionsEnabled = false
assertionsEnabled: Boolean = false
scala> boolAssert(x / 0 == 0)
java.lang.ArithmeticException: / by zero
... 33 elided
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But attempting to assert on the same code in byNameAssert’s case will not
yield an exception:
scala> byNameAssert(x / 0 == 0)
9.6
Conclusion
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This chapter has shown you how to build on Scala’s rich function support
to build control abstractions. You can use functions within your code to
factor out common control patterns, and you can take advantage of higherorder functions in the Scala library to reuse control patterns that are common
across all programmers’ code. We also discussed how to use currying and
by-name parameters so that your own higher-order functions can be used
with a concise syntax.
In the previous chapter and this one, you have seen quite a lot of information about functions. The next few chapters will go back to discussing
more object-oriented features of the language.
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Chapter 10
Composition and Inheritance
Chapter 6 introduced some basic object-oriented aspects of Scala. This chapter picks up where Chapter 6 left off and dives into Scala’s support for objectoriented programming in much greater detail.
We’ll compare two fundamental relationships between classes: composition and inheritance. Composition means one class holds a reference to
another, using the referenced class to help it fulfill its mission. Inheritance is
the superclass/subclass relationship.
In addition to these topics, we’ll discuss abstract classes, parameterless
methods, extending classes, overriding methods and fields, parametric fields,
invoking superclass constructors, polymorphism and dynamic binding, final
members and classes, and factory objects and methods.
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10.1
A two-dimensional layout library
As a running example in this chapter, we’ll create a library for building and
rendering two-dimensional layout elements. Each element will represent a
rectangle filled with text. For convenience, the library will provide factory
methods named “elem” that construct new elements from passed data. For
example, you’ll be able to create a layout element containing a string using
a factory method with the following signature:
elem(s: String): Element
As you can see, elements will be modeled with a type named Element.
You’ll be able to call above or beside on an element, passing in a second element, to get a new element that combines the two. For example,
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the following expression would construct a larger element consisting of two
columns, each with a height of two:
val column1 = elem("hello") above elem("***")
val column2 = elem("***") above elem("world")
column1 beside column2
Printing the result of this expression would give you:
hello ***
*** world
Layout elements are a good example of a system in which objects can be
constructed from simple parts with the aid of composing operators. In this
chapter, we’ll define classes that enable element objects to be constructed
from arrays, lines, and rectangles. These basic element objects will be the
simple parts. We’ll also define composing operators above and beside.
Such composing operators are also often called combinators because they
combine elements of some domain into new elements.
Thinking in terms of combinators is generally a good way to approach
library design: it pays to think about the fundamental ways to construct objects in an application domain. What are the simple objects? In what ways
can more interesting objects be constructed out of simpler ones? How do
combinators hang together? What are the most general combinations? Do
they satisfy any interesting laws? If you have good answers to these questions, your library design is on track.
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10.2
Abstract classes
Our first task is to define type Element, which represents layout elements.
Since elements are two dimensional rectangles of characters, it makes sense
to include a member, contents, that refers to the contents of a layout element. The contents can be represented as an array of strings, where each
string represents a line. Hence, the type of the result returned by contents
will be Array[String]. Listing 10.1 shows what it will look like.
In this class, contents is declared as a method that has no implementation. In other words, the method is an abstract member of class Element. A
class with abstract members must itself be declared abstract, which is done
by writing an abstract modifier in front of the class keyword:
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abstract class Element {
def contents: Array[String]
}
Listing 10.1 · Defining an abstract method and class.
abstract class Element ...
The abstract modifier signifies that the class may have abstract members
that do not have an implementation. As a result, you cannot instantiate an
abstract class. If you try to do so, you’ll get a compiler error:
scala> new Element
:5: error: class Element is abstract;
cannot be instantiated
new Element
ˆ
Later in this chapter, you’ll see how to create subclasses of class Element,
which you’ll be able to instantiate because they fill in the missing definition
for contents.
Note that the contents method in class Element does not carry an
abstract modifier. A method is abstract if it does not have an implementation (i.e., no equals sign or body). Unlike Java, no abstract modifier is
necessary (or allowed) on method declarations. Methods that have an implementation are called concrete.
Another bit of terminology distinguishes between declarations and definitions. Class Element declares the abstract method contents, but currently
defines no concrete methods. In the next section, however, we’ll enhance
Element by defining some concrete methods.
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10.3
Defining parameterless methods
As a next step, we’ll add methods to Element that reveal its width and height,
as shown in Listing 10.2. The height method returns the number of lines in
contents. The width method returns the length of the first line, or if there
are no lines in the element, returns zero. (This means you cannot define an
element with a height of zero and a non-zero width.)
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abstract class Element {
def contents: Array[String]
def height: Int = contents.length
def width: Int = if (height == 0) 0 else contents(0).length
}
Listing 10.2 · Defining parameterless methods width and height.
Note that none of Element’s three methods has a parameter list, not even
an empty one. For example, instead of:
def width(): Int
the method is defined without parentheses:
def width: Int
Such parameterless methods are quite common in Scala. By contrast,
methods defined with empty parentheses, such as def height(): Int, are
called empty-paren methods. The recommended convention is to use a parameterless method whenever there are no parameters and the method accesses mutable state only by reading fields of the containing object (in particular, it does not change mutable state). This convention supports the uniform access principle,1 which says that client code should not be affected by
a decision to implement an attribute as a field or method.
For instance, we could implement width and height as fields, instead
of methods, simply by changing the def in each definition to a val:
abstract class Element {
def contents: Array[String]
val height = contents.length
val width =
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if (height == 0) 0 else contents(0).length
}
The two pairs of definitions are completely equivalent from a client’s point
of view. The only difference is that field accesses might be slightly faster
than method invocations because the field values are pre-computed when the
1 Meyer,
Object-Oriented Software Construction [Mey00]
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class is initialized, instead of being computed on each method call. On the
other hand, the fields require extra memory space in each Element object.
So it depends on the usage profile of a class whether an attribute is better
represented as a field or method, and that usage profile might change over
time. The point is that clients of the Element class should not be affected
when its internal implementation changes.
In particular, a client of class Element should not need to be rewritten
if a field of that class gets changed into an access function, so long as the
access function is pure (i.e., it does not have any side effects and does not
depend on mutable state). The client should not need to care either way.
So far so good. But there’s still a slight complication that has to do
with the way Java handles things. The problem is that Java does not implement the uniform access principle. So it’s string.length() in Java,
not string.length, even though it’s array.length, not array.length().
Needless to say, this is very confusing.
To bridge that gap, Scala is very liberal when it comes to mixing parameterless and empty-paren methods. In particular, you can override a parameterless method with an empty-paren method, and vice versa. You can also
leave off the empty parentheses on an invocation of any function that takes
no arguments. For instance, the following two lines are both legal in Scala:
Array(1, 2, 3).toString
"abc".length
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In principle it’s possible to leave out all empty parentheses in Scala function
calls. However, it’s still recommended to write the empty parentheses when
the invoked method represents more than a property of its receiver object.
For instance, empty parentheses are appropriate if the method performs I/O,
writes reassignable variables (vars), or reads vars other than the receiver’s
fields, either directly or indirectly by using mutable objects. That way, the
parameter list acts as a visual clue that some interesting computation is triggered by the call. For instance:
"hello".length
// no () because no side-effect
println()
// better to not drop the ()
To summarize, it is encouraged in Scala to define methods that take no parameters and have no side effects as parameterless methods (i.e., leaving off
the empty parentheses). On the other hand, you should never define a method
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that has side-effects without parentheses, because invocations of that method
would then look like a field selection. So your clients might be surprised to
see the side effects.
Similarly, whenever you invoke a function that has side effects, be sure to
include the empty parentheses when you write the invocation. Another way
to think about this is if the function you’re calling performs an operation,
use the parentheses. But if it merely provides access to a property, leave the
parentheses off.
10.4
Extending classes
We still need to be able to create new element objects. You have already
seen that “new Element” cannot be used for this because class Element is
abstract. To instantiate an element, therefore, we will need to create a subclass that extends Element and implements the abstract contents method.
Listing 10.3 shows one possible way to do that:
class ArrayElement(conts: Array[String]) extends Element {
def contents: Array[String] = conts
}
Listing 10.3 · Defining ArrayElement as a subclass of Element.
Class ArrayElement is defined to extend class Element. Just like in
Java, you use an extends clause after the class name to express this:
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... extends Element ...
Such an extends clause has two effects: It makes class ArrayElement
inherit all non-private members from class Element, and it makes the type
ArrayElement a subtype of the type Element. Given ArrayElement extends Element, class ArrayElement is called a subclass of class Element.
Conversely, Element is a superclass of ArrayElement. If you leave out
an extends clause, the Scala compiler implicitly assumes your class extends from scala.AnyRef, which on the Java platform is the same as class
java.lang.Object. Thus, class Element implicitly extends class AnyRef.
You can see these inheritance relationships in Figure 10.1.
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scala
AnyRef
«java.lang.Object»
Element
«abstract»
ArrayElement
Array[String]
Figure 10.1 · Class diagram for ArrayElement.
Inheritance means that all members of the superclass are also members
of the subclass, with two exceptions. First, private members of the superclass are not inherited in a subclass. Second, a member of a superclass is
not inherited if a member with the same name and parameters is already implemented in the subclass. In that case we say the member of the subclass
overrides the member of the superclass. If the member in the subclass is
concrete and the member of the superclass is abstract, we also say that the
concrete member implements the abstract one.
For example, the contents method in ArrayElement overrides (or alternatively: implements) abstract method contents in class Element.2 By
contrast, class ArrayElement inherits the width and height methods from
class Element. For example, given an ArrayElement ae, you can query its
width using ae.width, as if width were defined in class ArrayElement:
scala> val ae = new ArrayElement(Array("hello", "world"))
ae: ArrayElement = ArrayElement@39274bf7
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scala> ae.width
2 One
flaw with this design is that because the returned array is mutable, clients could
change it. For the book we’ll keep things simple, but were ArrayElement part of a real
project, you might consider returning a defensive copy of the array instead. Another problem
is we aren’t currently ensuring that every String element of the contents array has the
same length. This could be solved by checking the precondition in the primary constructor
and throwing an exception if it is violated.
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res0: Int = 5
Subtyping means that a value of the subclass can be used wherever a
value of the superclass is required. For example:
val e: Element = new ArrayElement(Array("hello"))
Variable e is defined to be of type Element, so its initializing value should
also be an Element. In fact, the initializing value’s type is ArrayElement.
This is OK, because class ArrayElement extends class Element, and as a
result, the type ArrayElement is compatible with the type Element.3
Figure 10.1 also shows the composition relationship that exists between
ArrayElement and Array[String]. This relationship is called composition
because class ArrayElement is “composed” out of class Array[String],
in that the Scala compiler will place into the binary class it generates for
ArrayElement a field that holds a reference to the passed conts array. We’ll
discuss some design considerations concerning composition and inheritance
later in this chapter, in Section 10.11.
10.5
Overriding methods and fields
The uniform access principle is just one aspect where Scala treats fields and
methods more uniformly than Java. Another difference is that in Scala, fields
and methods belong to the same namespace. This makes it possible for a
field to override a parameterless method. For instance, you could change
the implementation of contents in class ArrayElement from a method to
a field without having to modify the abstract method definition of contents
in class Element, as shown in Listing 10.4:
class ArrayElement(conts: Array[String]) extends Element {
val contents: Array[String] = conts
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}
Listing 10.4 · Overriding a parameterless method with a field.
Field contents (defined with a val) in this version of ArrayElement
is a perfectly good implementation of the parameterless method contents
3 For
more perspective on the difference between subclass and subtype, see the glossary
entry for subtype.
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(declared with a def) in class Element. On the other hand, in Scala it is
forbidden to define a field and method with the same name in the same class,
whereas this is allowed in Java.
For example, this Java class would compile just fine:
// This is Java
class CompilesFine {
private int f = 0;
public int f() {
return 1;
}
}
But the corresponding Scala class would not compile:
class WontCompile {
private var f = 0 // Won't compile, because a field
def f = 1
// and method have the same name
}
Generally, Scala has just two namespaces for definitions in place of Java’s
four. Java’s four namespaces are fields, methods, types, and packages. By
contrast, Scala’s two namespaces are:
• values (fields, methods, packages, and singleton objects)
• types (class and trait names)
The reason Scala places fields and methods into the same namespace is precisely so you can override a parameterless method with a val, something
you can’t do with Java.4
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10.6
Defining parametric fields
Consider again the definition of class ArrayElement shown in the previous
section. It has a parameter conts whose sole purpose is to be copied into the
4 The
reason that packages share the same namespace as fields and methods in Scala is
to enable you to import packages (in addition to just the names of types) and the fields and
methods of singleton objects. This is also something you can’t do in Java. It will be described
in Section 13.3.
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Section 10.6
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contents field. The name conts of the parameter was chosen just so that
it would look similar to the field name contents without actually clashing
with it. This is a “code smell,” a sign that there may be some unnecessary
redundancy and repetition in your code.
You can avoid the code smell by combining the parameter and the field
in a single parametric field definition, as shown in Listing 10.5:
class ArrayElement(
val contents: Array[String]
) extends Element
Listing 10.5 · Defining contents as a parametric field.
Note that now the contents parameter is prefixed by val. This is a shorthand that defines at the same time a parameter and field with the same
name. Specifically, class ArrayElement now has an (unreassignable) field
contents, which can be accessed from outside the class. The field is initialized with the value of the parameter. It’s as if the class had been written as
follows, where x123 is an arbitrary fresh name for the parameter:
class ArrayElement(x123: Array[String]) extends Element {
val contents: Array[String] = x123
}
You can also prefix a class parameter with var, in which case the corresponding field would be reassignable. Finally, it is possible to add modifiers,
such as private, protected,5 or override to these parametric fields, just
as you can for any other class member. Consider, for instance, the following
class definitions:
class Cat {
val dangerous = false
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}
class Tiger(
override val dangerous: Boolean,
private var age: Int
) extends Cat
5 The protected modifier,
which grants access to subclasses, will be covered in detail in
Chapter 13.
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Section 10.7
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Tiger’s definition is a shorthand for the following alternate class definition
with an overriding member dangerous and a private member age:
class Tiger(param1: Boolean, param2: Int) extends Cat {
override val dangerous = param1
private var age = param2
}
Both members are initialized from the corresponding parameters. We chose
the names of those parameters, param1 and param2, arbitrarily. The important thing was that they not clash with any other name in scope.
10.7
Invoking superclass constructors
You now have a complete system consisting of two classes: an abstract class
Element, which is extended by a concrete class ArrayElement. You might
also envision other ways to express an element. For example, clients might
want to create a layout element consisting of a single line given by a string.
Object-oriented programming makes it easy to extend a system with new
data-variants. You can simply add subclasses. For example, Listing 10.6
shows a LineElement class that extends ArrayElement:
class LineElement(s: String) extends ArrayElement(Array(s)) {
override def width = s.length
override def height = 1
}
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Listing 10.6 · Invoking a superclass constructor.
Since LineElement extends ArrayElement, and ArrayElement’s constructor takes a parameter (an Array[String]), LineElement needs to pass
an argument to the primary constructor of its superclass. To invoke a superclass constructor, you simply place the argument or arguments you want to
pass in parentheses following the name of the superclass. For example, class
LineElement passes Array(s) to ArrayElement’s primary constructor by
placing it in parentheses after the superclass ArrayElement’s name:
... extends ArrayElement(Array(s)) ...
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Element
«abstract»
ArrayElement
Array[String]
LineElement
Figure 10.2 · Class diagram for LineElement.
With the new subclass, the inheritance hierarchy for layout elements now
looks as shown in Figure 10.2.
10.8
Using override modifiers
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Note that the definitions of width and height in LineElement carry an
override modifier. In Section 6.3, you saw this modifier in the definition
of a toString method. Scala requires such a modifier for all members that
override a concrete member in a parent class. The modifier is optional if a
member implements an abstract member with the same name. The modifier
is forbidden if a member does not override or implement some other member
in a base class. Since height and width in class LineElement override
concrete definitions in class Element, override is required.
This rule provides useful information for the compiler that helps avoid
some hard-to-catch errors and makes system evolution safer. For instance, if
you happen to misspell the method or accidentally give it a different parameter list, the compiler will respond with an error message:
$ scalac LineElement.scala
.../LineElement.scala:50:
error: method hight overrides nothing
override def hight = 1
ˆ
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The override convention is even more important when it comes to system
evolution. Say you defined a library of 2D drawing methods. You made it
publicly available, and it is widely used. In the next version of the library
you want to add to your base class Shape a new method with this signature:
def hidden(): Boolean
Your new method will be used by various drawing methods to determine
whether a shape needs to be drawn. This could lead to a significant speedup,
but you cannot do this without the risk of breaking client code. After all, a
client could have defined a subclass of Shape with a different implementation
of hidden. Perhaps the client’s method actually makes the receiver object
disappear instead of testing whether the object is hidden. Because the two
versions of hidden override each other, your drawing methods would end up
making objects disappear, which is certainly not what you want!
These “accidental overrides” are the most common manifestation of what
is called the “fragile base class” problem. The problem is that if you add new
members to base classes (which we usually call superclasses) in a class hierarchy, you risk breaking client code. Scala cannot completely solve the fragile base class problem, but it improves on the situation compared to Java.6
If the drawing library and its clients were written in Scala, then the client’s
original implementation of hidden could not have had an override modifier, because at the time there was no other method with that name.
Once you add the hidden method to the second version of your shape
class, a recompile of the client would give an error like the following:
.../Shapes.scala:6: error: error overriding method
hidden in class Shape of type ()Boolean;
method hidden needs `override' modifier
def hidden(): Boolean =
ˆ
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That is, instead of wrong behavior your client would get a compile-time
error, which is usually much preferable.
6 In Java 1.5, an @Override annotation was introduced that works similarly to Scala’s
override modifier, but unlike Scala’s override, is not required.
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Section 10.9
10.9
Chapter 10 · Composition and Inheritance
Polymorphism and dynamic binding
You saw in Section 10.4 that a variable of type Element could refer to an
object of type ArrayElement. The name for this phenomenon is polymorphism, which means “many shapes” or “many forms.” In this case, Element
objects can have many forms.7
So far, you’ve seen two such forms: ArrayElement and LineElement.
You can create more forms of Element by defining new Element subclasses.
For example, you could define a new form of Element that has a given width
and height, and is filled everywhere with a given character:
class UniformElement(
ch: Char,
override val width: Int,
override val height: Int
) extends Element {
private val line = ch.toString * width
def contents = Array.fill(height)(line)
}
The inheritance hierarchy for class Element now looks as shown in Figure 10.3. As a result, Scala will accept all of the following assignments,
because the type of the assigning expression conforms to the type of the
defined variable:
val e1: Element = new ArrayElement(Array("hello", "world"))
val ae: ArrayElement = new LineElement("hello")
val e2: Element = ae
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val e3: Element = new UniformElement('x', 2, 3)
If you check the inheritance hierarchy, you’ll find that in each of these four
val definitions, the type of the expression to the right of the equals sign is
below the type of the val being initialized to the left of the equals sign.
The other half of the story, however, is that method invocations on variables and expressions are dynamically bound. This means that the actual
method implementation invoked is determined at run time based on the class
of the object, not the type of the variable or expression. To demonstrate this
7 This
kind of polymorphism is called subtyping polymorphism. Another kind of polymorphism in Scala called universal polymorphism is discussed in Chapter 19.
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Section 10.9
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Element
«abstract»
UniformElement
ArrayElement
LineElement
Figure 10.3 · Class hierarchy of layout elements.
behavior, we’ll temporarily remove all existing members from our Element
classes and add a method named demo to Element. We’ll override demo in
ArrayElement and LineElement, but not in UniformElement:
abstract class Element {
def demo() = {
println("Element's implementation invoked")
}
}
class ArrayElement extends Element {
override def demo() = {
println("ArrayElement's implementation invoked")
}
}
class LineElement extends ArrayElement {
override def demo() = {
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println("LineElement's implementation invoked")
}
}
// UniformElement inherits Element's demo
class UniformElement extends Element
If you enter this code into the interpreter, you can then define this method
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Section 10.10
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that takes an Element and invokes demo on it:
def invokeDemo(e: Element) = {
e.demo()
}
If you pass an ArrayElement to invokeDemo, you’ll see a message indicating ArrayElement’s implementation of demo was invoked, even though the
type of the variable, e, on which demo was invoked is Element:
scala> invokeDemo(new ArrayElement)
ArrayElement's implementation invoked
Similarly, if you pass a LineElement to invokeDemo, you’ll see a message
that indicates LineElement’s demo implementation was invoked:
scala> invokeDemo(new LineElement)
LineElement's implementation invoked
The behavior when passing a UniformElement may at first glance look suspicious, but it is correct:
scala> invokeDemo(new UniformElement)
Element's implementation invoked
Because UniformElement does not override demo, it inherits the implementation of demo from its superclass, Element. Thus, Element’s implementation is the correct implementation of demo to invoke when the class of the
object is UniformElement.
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10.10
Declaring final members
Sometimes when designing an inheritance hierarchy, you want to ensure that
a member cannot be overridden by subclasses. In Scala, as in Java, you do
this by adding a final modifier to the member. As shown in Listing 10.7,
you could place a final modifier on ArrayElement’s demo method.
Given this version of ArrayElement, an attempt to override demo in its
subclass, LineElement, would not compile:
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Section 10.10
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class ArrayElement extends Element {
final override def demo() = {
println("ArrayElement's implementation invoked")
}
}
Listing 10.7 · Declaring a final method.
elem.scala:18: error: error overriding method demo
in class ArrayElement of type ()Unit;
method demo cannot override final member
override def demo() = {
ˆ
You may also at times want to ensure that an entire class not be subclassed. To do this you simply declare the entire class final by adding a
final modifier to the class declaration. For example, Listing 10.8 shows
how you would declare ArrayElement final:
final class ArrayElement extends Element {
override def demo() = {
println("ArrayElement's implementation invoked")
}
}
Listing 10.8 · Declaring a final class.
With this version of ArrayElement, any attempt at defining a subclass
would fail to compile:
elem.scala: 18: error: illegal inheritance from final class
ArrayElement
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class LineElement extends ArrayElement {
ˆ
We’ll now remove the final modifiers and demo methods, and go back
to the earlier implementation of the Element family. We’ll focus our attention in the remainder of this chapter to completing a working version of the
layout library.
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Section 10.11
10.11
Chapter 10 · Composition and Inheritance
Using composition and inheritance
Composition and inheritance are two ways to define a new class in terms
of another existing class. If what you’re after is primarily code reuse, you
should in general prefer composition to inheritance. Only inheritance suffers
from the fragile base class problem, in which you can inadvertently break
subclasses by changing a superclass.
One question you can ask yourself about an inheritance relationship is
whether it models an is-a relationship.8 For example, it would be reasonable
to say that ArrayElement is-an Element. Another question you can ask is
whether clients will want to use the subclass type as a superclass type.9 In
the case of ArrayElement, we do indeed expect clients will want to use an
ArrayElement as an Element.
If you ask these questions about the inheritance relationships shown in
Figure 10.3, do any of the relationships seem suspicious? In particular, does
it seem obvious to you that a LineElement is-an ArrayElement? Do you
think clients would ever need to use a LineElement as an ArrayElement?
In fact, we defined LineElement as a subclass of ArrayElement primarily to reuse ArrayElement’s definition of contents. Perhaps it would
be better, therefore, to define LineElement as a direct subclass of Element,
like this:
class LineElement(s: String) extends Element {
val contents = Array(s)
override def width = s.length
override def height = 1
}
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In the previous version, LineElement had an inheritance relationship with
ArrayElement, from which it inherited contents. It now has a composition
relationship with Array: it holds a reference to an array of strings from
its own contents field.10 Given this implementation of LineElement, the
inheritance hierarchy for Element now looks as shown in Figure 10.4.
8 Meyers,
Effective C++ [Mey91]
Thinking in Java [Eck98]
10 Class ArrayElement also has a composition relationship with Array, because its parametric contents field holds a reference to an array of strings. The code for ArrayElement
is shown in Listing 10.5 on page 227. Its composition relationship is represented in class
diagrams by a diamond, as shown, for example, in Figure 10.1 on page 224.
9 Eckel,
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Section 10.12
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Element
«abstract»
ArrayElement
LineElement
UniformElement
Figure 10.4 · Class hierarchy with revised LineElement.
10.12
Implementing above, beside, and toString
As a next step, we’ll implement method above in class Element. Putting
one element above another means concatenating the two contents values
of the elements. So a first draft of method above could look like this:
def above(that: Element): Element =
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new ArrayElement(this.contents ++ that.contents)
The ++ operation concatenates two arrays. Arrays in Scala are represented
as Java arrays, but support many more methods. Specifically, arrays in
Scala can be converted to instances of a class scala.Seq, which represents
sequence-like structures and contains a number of methods for accessing and
transforming sequences. Some other array methods will be explained in this
chapter and a more comprehensive discussion will be given in Chapter 17.
In fact, the code shown previously is not quite sufficient because it does
not let you put elements of different widths on top of each other. To keep
things simple in this section, however, we’ll leave this as is and only pass
elements of the same length to above. In Section 10.14, we’ll make an
enhancement to above so that clients can use it to combine elements of different widths.
The next method to implement is beside. To put two elements beside
each other, we’ll create a new element in which every line results from concatenating corresponding lines of the two elements. As before, to keep things
simple, we’ll start by assuming the two elements have the same height. This
leads to the following design of method beside:
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Section 10.12
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def beside(that: Element): Element = {
val contents = new Array[String](this.contents.length)
for (i <- 0 until this.contents.length)
contents(i) = this.contents(i) + that.contents(i)
new ArrayElement(contents)
}
The beside method first allocates a new array, contents, and fills it with
the concatenation of the corresponding array elements in this.contents
and that.contents. It finally produces a new ArrayElement containing
the new contents.
Although this implementation of beside works, it is in an imperative
style, the telltale sign of which is the loop in which we index through arrays.
Alternatively, the method could be abbreviated to one expression:
new ArrayElement(
for (
(line1, line2) <- this.contents zip that.contents
) yield line1 + line2
)
Here, the two arrays, this.contents and that.contents, are transformed
into an array of pairs (as Tuple2s are called) using the zip operator. The
zip operator picks corresponding elements in its two operands and forms an
array of pairs. For instance, this expression:
Array(1, 2, 3) zip Array("a", "b")
will evaluate to:
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Array((1, "a"), (2, "b"))
If one of the two operand arrays is longer than the other, zip will drop the
remaining elements. In the expression above, the third element of the left
operand, 3, does not form part of the result, because it does not have a corresponding element in the right operand.
The zipped array is then iterated over by a for expression. Here, the
syntax “for ((line1, line2) <- . . . )” allows you to name both elements
of a pair in one pattern (i.e., line1 stands now for the first element of the
pair, and line2 stands for the second). Scala’s pattern-matching system will
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Section 10.13
Chapter 10 · Composition and Inheritance
be described in detail in Chapter 15. For now, you can just think of this as a
way to define two vals, line1 and line2, for each step of the iteration.
The for expression has a yield part and therefore yields a result. The
result is of the same kind as the expression iterated over (i.e., it is an array).
Each element of the array is the result of concatenating the corresponding
lines, line1 and line2. So the end result of this code is the same as in
the first version of beside, but because it avoids explicit array indexing, the
result is obtained in a less error-prone way.
You still need a way to display elements. As usual, this is done by defining a toString method that returns an element formatted as a string. Here
is its definition:
override def toString = contents mkString "\n"
The implementation of toString makes use of mkString, which is defined
for all sequences, including arrays. As you saw in Section 7.8, an expression
like “arr mkString sep” returns a string consisting of all elements of the array arr. Each element is mapped to a string by calling its toString method.
A separator string sep is inserted between consecutive element strings. So
the expression, “contents mkString "\n"” formats the contents array as
a string, where each array element appears on a line by itself.
Note that toString does not carry an empty parameter list. This follows
the recommendations for the uniform access principle, because toString is
a pure method that does not take any parameters. With the addition of these
three methods, class Element now looks as shown in Listing 10.9.
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10.13
Defining a factory object
You now have a hierarchy of classes for layout elements. This hierarchy
could be presented to your clients “as is,” but you might also choose to hide
the hierarchy behind a factory object.
A factory object contains methods that construct other objects. Clients
would then use these factory methods to construct objects, rather than constructing the objects directly with new. An advantage of this approach is that
object creation can be centralized and the details of how objects are represented with classes can be hidden. This hiding will both make your library
simpler for clients to understand, because less detail is exposed, and provide
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abstract class Element {
def contents: Array[String]
def width: Int =
if (height == 0) 0 else contents(0).length
def height: Int = contents.length
def above(that: Element): Element =
new ArrayElement(this.contents ++ that.contents)
def beside(that: Element): Element =
new ArrayElement(
for (
(line1, line2) <- this.contents zip that.contents
) yield line1 + line2
)
override def toString = contents mkString "\n"
}
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Listing 10.9 · Class Element with above, beside, and toString.
you with more opportunities to change your library’s implementation later
without breaking client code.
The first task in constructing a factory for layout elements is to choose
where the factory methods should be located. Should they be members of a
singleton object or of a class? What should the containing object or class be
called? There are many possibilities. A straightforward solution is to create a
companion object of class Element and make this the factory object for layout elements. That way, you need to expose only the class/object combo of
Element to your clients, and you can hide the three implementation classes
ArrayElement, LineElement, and UniformElement.
Listing 10.10 is a design of the Element object that follows this scheme.
The Element object contains three overloaded variants of an elem method
and each constructs a different kind of layout object.
With the advent of these factory methods, it makes sense to change the
implementation of class Element so that it goes through the elem factory
methods rather than creating new ArrayElement instances explicitly. To call
the factory methods without qualifying them with Element, the name of the
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Section 10.14
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object Element {
def elem(contents: Array[String]): Element =
new ArrayElement(contents)
def elem(chr: Char, width: Int, height: Int): Element =
new UniformElement(chr, width, height)
def elem(line: String): Element =
new LineElement(line)
}
Listing 10.10 · A factory object with factory methods.
singleton object, we will import Element.elem at the top of the source file.
In other words, instead of invoking the factory methods with Element.elem
inside class Element, we’ll import Element.elem so we can just call the
factory methods by their simple name, elem. Listing 10.11 shows what class
Element will look like after these changes.
In addition, given the factory methods, the subclasses, ArrayElement,
LineElement, and UniformElement, could now be private because they
no longer need to be accessed directly by clients. In Scala, you can define classes and singleton objects inside other classes and singleton objects.
One way to make the Element subclasses private is to place them inside the
Element singleton object and declare them private there. The classes will
still be accessible to the three elem factory methods, where they are needed.
Listing 10.12 shows how that will look.
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10.14
Heighten and widen
We need one last enhancement. The version of Element shown in Listing 10.11 is not quite sufficient because it does not allow clients to place
elements of different widths on top of each other, or place elements of different heights beside each other.
For example, evaluating the following expression won’t work correctly,
because the second line in the combined element is longer than the first:
new ArrayElement(Array("hello")) above
new ArrayElement(Array("world!"))
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Section 10.14
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import Element.elem
abstract class Element {
def contents: Array[String]
def width: Int =
if (height == 0) 0 else contents(0).length
def height: Int = contents.length
def above(that: Element): Element =
elem(this.contents ++ that.contents)
def beside(that: Element): Element =
elem(
for (
(line1, line2) <- this.contents zip that.contents
) yield line1 + line2
)
override def toString = contents mkString "\n"
}
Listing 10.11 · Class Element refactored to use factory methods.
Similarly, evaluating the following expression would not work properly,
because the first ArrayElement has a height of two and the second a height
of only one:
new ArrayElement(Array("one", "two")) beside
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new ArrayElement(Array("one"))
Listing 10.13 shows a private helper method, widen, which takes a width
and returns an Element of that width. The result contains the contents of
this Element, centered, padded to the left and right by any spaces needed
to achieve the required width. Listing 10.13 also shows a similar method,
heighten, which performs the same function in the vertical direction. The
widen method is invoked by above to ensure that Elements placed above
each other have the same width. Similarly, the heighten method is invoked
by beside to ensure that elements placed beside each other have the same
height. With these changes, the layout library is ready for use.
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Section 10.14
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object Element {
private class ArrayElement(
val contents: Array[String]
) extends Element
private class LineElement(s: String) extends Element {
val contents = Array(s)
override def width = s.length
override def height = 1
}
private class UniformElement(
ch: Char,
override val width: Int,
override val height: Int
) extends Element {
private val line = ch.toString * width
def contents = Array.fill(height)(line)
}
def elem(contents:
Array[String]): Element =
new ArrayElement(contents)
def elem(chr: Char, width: Int, height: Int): Element =
new UniformElement(chr, width, height)
def elem(line: String): Element =
new LineElement(line)
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}
Listing 10.12 · Hiding implementation with private classes.
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242
Section 10.14
Chapter 10 · Composition and Inheritance
import Element.elem
abstract class Element {
def contents:
Array[String]
def width: Int = contents(0).length
def height: Int = contents.length
def above(that: Element): Element = {
val this1 = this widen that.width
val that1 = that widen this.width
elem(this1.contents ++ that1.contents)
}
def beside(that: Element): Element = {
val this1 = this heighten that.height
val that1 = that heighten this.height
elem(
for ((line1, line2) <- this1.contents zip that1.contents)
yield line1 + line2)
}
def widen(w: Int): Element =
if (w <= width) this
else {
val left = elem(' ', (w - width) / 2, height)
val right = elem(' ', w - width - left.width, height)
left beside this beside right
}
def heighten(h: Int): Element =
if (h <= height) this
else {
val top = elem(' ', width, (h - height) / 2)
val bot = elem(' ', width, h - height - top.height)
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top above this above bot
}
override def toString = contents mkString "\n"
}
Listing 10.13 · Element with widen and heighten methods.
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243
Section 10.15
10.15
Chapter 10 · Composition and Inheritance
Putting it all together
A fun way to exercise almost all elements of the layout library is to write
a program that draws a spiral with a given number of edges. This Spiral
program, shown in Listing 10.14, will do just that.
import Element.elem
object Spiral {
val space = elem(" ")
val corner = elem("+")
def spiral(nEdges: Int, direction: Int): Element = {
if (nEdges == 1)
elem("+")
else {
val sp = spiral(nEdges - 1, (direction + 3) % 4)
def verticalBar = elem('|', 1, sp.height)
def horizontalBar = elem('-', sp.width, 1)
if (direction == 0)
(corner beside horizontalBar) above (sp beside space)
else if (direction == 1)
(sp above space) beside (corner above verticalBar)
else if (direction == 2)
(space beside sp) above (horizontalBar beside corner)
else
(verticalBar above corner) beside (space above sp)
}
}
def main(args: Array[String]) = {
val nSides = args(0).toInt
println(spiral(nSides, 0))
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}
}
Listing 10.14 · The Spiral application.
Because Spiral is a standalone object with a main method with the
proper signature, it is a Scala application. Spiral takes one command-line
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244
Section 10.16
Chapter 10 · Composition and Inheritance
245
argument, an integer, and draws a spiral with the specified number of edges.
For example, you could draw a six-edge spiral, as shown on the left, and
larger spirals, as shown on the right.
$ scala Spiral 6
$ scala Spiral 11
$ scala Spiral 17
+-----
+----------
+----------------
|
|
|
| +-+
| +------+
| +------------+
| + |
| |
| |
|
| | +--+ |
|
+---+
| | |
|
| |
|
| | +--------+ |
| | |
| |
| | ++ | |
| | | +----+ | |
| |
| | | |
| |
| | |
| +----+ |
| | | | ++ | | |
|
| | | |
|
+--------+
| | | |
| | | +--+ | | |
| | |
| | |
| | +------+ | |
| |
| |
| +----------+ |
|
|
+--------------+
10.16
Conclusion
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In this section, you saw more concepts related to object-oriented programming in Scala. Among others, you encountered abstract classes, inheritance
and subtyping, class hierarchies, parametric fields, and method overriding.
You should have developed a feel for constructing a non-trivial class hierarchy in Scala. We’ll work with the layout library again in Chapter 14.
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Chapter 11
Scala’s Hierarchy
Now that you’ve seen the details of class inheritance in the previous chapter,
it is a good time to take a step back and look at Scala’s class hierarchy as a
whole. In Scala, every class inherits from a common superclass named Any.
Because every class is a subclass of Any, the methods defined in Any are
“universal” methods: they may be invoked on any object. Scala also defines
some interesting classes at the bottom of the hierarchy, Null and Nothing,
which essentially act as common subclasses. For example, just as Any is a
superclass of every other class, Nothing is a subclass of every other class.
In this chapter, we’ll give you a tour of Scala’s class hierarchy.
11.1
Scala’s class hierarchy
Figure 11.1 shows an outline of Scala’s class hierarchy. At the top of the
hierarchy is class Any, which defines methods that include the following:
final def ==(that: Any): Boolean
final def !=(that: Any): Boolean
def equals(that: Any): Boolean
def ##: Int
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def hashCode: Int
def toString: String
Because every class inherits from Any, every object in a Scala program can
be compared using ==, !=, or equals; hashed using ## or hashCode; and
formatted using toString. The equality and inequality methods, == and !=,
are declared final in class Any, so they cannot be overridden in subclasses.
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Section 11.1
Chapter 11 · Scala’s Hierarchy
The == method is essentially the same as equals and != is always the negation of equals.1 So individual classes can tailor what == or != means by
overriding the equals method. We’ll show an example later in this chapter.
The root class Any has two subclasses: AnyVal and AnyRef. AnyVal is
the parent class of value classes in Scala. While you can define your own
value classes (see Section 11.4), there are nine value classes built into Scala:
Byte, Short, Char, Int, Long, Float, Double, Boolean, and Unit. The
first eight of these correspond to Java’s primitive types, and their values are
represented at run time as Java’s primitive values. The instances of these
classes are all written as literals in Scala. For example, 42 is an instance of
Int, 'x' is an instance of Char, and false an instance of Boolean. You
cannot create instances of these classes using new. This is enforced by the
“trick” that value classes are all defined to be both abstract and final.
So if you were to write:
scala> new Int
you would get:
:5: error: class Int is abstract; cannot be
instantiated
new Int
ˆ
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The other value class, Unit, corresponds roughly to Java’s void type; it is
used as the result type of a method that does not otherwise return an interesting result. Unit has a single instance value, which is written (), as discussed
in Section 7.2.
As explained in Chapter 5, the value classes support the usual arithmetic
and boolean operators as methods. For instance, Int has methods named +
and *, and Boolean has methods named || and &&. Value classes also inherit
all methods from class Any. You can test this in the interpreter:
1 The only case where == does not directly call equals is for Java’s boxed numeric
classes, such as Integer or Long. In Java, a new Integer(1) does not equal a new Long(1)
even though for primitive values 1 == 1L. Since Scala is a more regular language than Java, it
was necessary to correct this discrepancy by special-casing the == method for these classes.
Likewise, the ## method provides a Scala version of hashing that is the same as Java’s
hashCode, except for boxed numeric types, where it works consistently with ==. For instance new Integer(1) and new Long(1) hash the same with ## even though their Java
hashCodes are different.
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247
scala
scala
Int
Long
scala
Float
scala
Double
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scala
Byte
scala
Short
scala
scala
Char
scala
Boolean
scala
Unit
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scala
Seq
Figure 11.1 · Class hierarchy of Scala.
scala
scala
Iterable
Nothing
... (other Scala value classes) ...
AnyVal
scala
Any
List
scala
scala
AnyRef
java.lang
String
scala
Null
... (other Scala classes) ...
... (other Java classes) ...
«java.lang.Object»
Subtype
Implicit Conversion
Section 11.1
Chapter 11 · Scala’s Hierarchy
248
Section 11.1
Chapter 11 · Scala’s Hierarchy
scala> 42.toString
res1: String = 42
scala> 42.hashCode
res2: Int = 42
scala> 42 equals 42
res3: Boolean = true
Note that the value class space is flat; all value classes are subtypes of
scala.AnyVal, but they do not subclass each other. Instead there are implicit conversions between different value class types. For example, an instance of class scala.Int is automatically widened (by an implicit conversion) to an instance of class scala.Long when required.
As mentioned in Section 5.10, implicit conversions are also used to add
more functionality to value types. For instance, the type Int supports all of
the operations below:
scala> 42 max 43
res4: Int = 43
scala> 42 min 43
res5: Int = 42
scala> 1 until 5
res6: scala.collection.immutable.Range = Range(1, 2, 3, 4)
scala> 1 to 5
res7: scala.collection.immutable.Range.Inclusive
= Range(1, 2, 3, 4, 5)
scala> 3.abs
res8: Int = 3
scala> (-3).abs
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res9: Int = 3
Here’s how this works: The methods min, max, until, to, and abs are all
defined in a class scala.runtime.RichInt, and there is an implicit conversion from class Int to RichInt. The conversion is applied whenever a
method is invoked on an Int that is undefined in Int but defined in RichInt.
Similar “booster classes” and implicit conversions exist for the other value
classes. Implicit conversions will be discussed in detail in Chapter 21.
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249
Section 11.2
Chapter 11 · Scala’s Hierarchy
The other subclass of the root class Any is class AnyRef. This is the base
class of all reference classes in Scala. As mentioned previously, on the Java
platform AnyRef is in fact just an alias for class java.lang.Object. So
classes written in Java, as well as classes written in Scala, all inherit from
AnyRef.2 One way to think of java.lang.Object, therefore, is as the way
AnyRef is implemented on the Java platform. Thus, although you can use
Object and AnyRef interchangeably in Scala programs on the Java platform,
the recommended style is to use AnyRef everywhere.
11.2
How primitives are implemented
How is all this implemented? In fact, Scala stores integers in the same way
as Java—as 32-bit words. This is important for efficiency on the JVM and
also for interoperability with Java libraries. Standard operations like addition
or multiplication are implemented as primitive operations. However, Scala
uses the “backup” class java.lang.Integer whenever an integer needs to
be seen as a (Java) object. This happens for instance when invoking the
toString method on an integer number or when assigning an integer to a
variable of type Any. Integers of type Int are converted transparently to
“boxed integers” of type java.lang.Integer whenever necessary.
All this sounds a lot like auto-boxing in Java 5 and it is indeed quite
similar. There’s one crucial difference though: Boxing in Scala is much less
visible than boxing in Java. Try the following in Java:
// This is Java
boolean isEqual(int x, int y) {
return x == y;
}
System.out.println(isEqual(421, 421));
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You will surely get true. Now, change the argument types of isEqual to
java.lang.Integer (or Object, the result will be the same):
2 The
reason AnyRef alias exists, instead of just using the name java.lang.Object, is
because Scala was originally designed to work on both the Java and .NET platforms. On
.NET, AnyRef was an alias for System.Object.
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250
Section 11.2
Chapter 11 · Scala’s Hierarchy
// This is Java
boolean isEqual(Integer x, Integer y) {
return x == y;
}
System.out.println(isEqual(421, 421));
You will find that you get false! What happens is that the number 421
gets boxed twice, so that the arguments for x and y are two different objects.
Because == means reference equality on reference types, and Integer is a
reference type, the result is false. This is one aspect where it shows that
Java is not a pure object-oriented language. There is a difference between
primitive types and reference types that can be clearly observed.
Now try the same experiment in Scala:
scala> def isEqual(x: Int, y: Int) = x == y
isEqual: (x: Int, y: Int)Boolean
scala> isEqual(421, 421)
res10: Boolean = true
scala> def isEqual(x: Any, y: Any) = x == y
isEqual: (x: Any, y: Any)Boolean
scala> isEqual(421, 421)
res11: Boolean = true
The equality operation == in Scala is designed to be transparent with
respect to the type’s representation. For value types, it is the natural (numeric
or boolean) equality. For reference types other than Java’s boxed numeric
types, == is treated as an alias of the equals method inherited from Object.
That method is originally defined as reference equality, but is overridden by
many subclasses to implement their natural notion of equality. This also
means that in Scala you never fall into Java’s well-known trap concerning
string comparisons. In Scala, string comparison works as it should:
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scala> val x = "abcd".substring(2)
x: String = cd
scala> val y = "abcd".substring(2)
y: String = cd
scala> x == y
res12: Boolean = true
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251
Section 11.3
Chapter 11 · Scala’s Hierarchy
In Java, the result of comparing x with y would be false. The programmer
should have used equals in this case, but it is easy to forget.
However, there are situations where you need reference equality instead
of user-defined equality. For example, in some situations where efficiency is
paramount, you would like to hash cons with some classes and compare their
instances with reference equality.3 For these cases, class AnyRef defines
an additional eq method, which cannot be overridden and is implemented
as reference equality (i.e., it behaves like == in Java for reference types).
There’s also the negation of eq, which is called ne. For example:
scala> val x = new String("abc")
x: String = abc
scala> val y = new String("abc")
y: String = abc
scala> x == y
res13: Boolean = true
scala> x eq y
res14: Boolean = false
scala> x ne y
res15: Boolean = true
Equality in Scala is discussed further in Chapter 30.
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11.3
Bottom types
At the bottom of the type hierarchy in Figure 11.1 you see the two classes
scala.Null and scala.Nothing. These are special types that handle some
“corner cases” of Scala’s object-oriented type system in a uniform way.
Class Null is the type of the null reference; it is a subclass of every
reference class (i.e., every class that itself inherits from AnyRef). Null is not
compatible with value types. You cannot, for example, assign a null value
to an integer variable:
3 You hash cons instances of a class by caching all instances you have created in a weak
collection. Then, any time you want a new instance of the class, you first check the cache.
If the cache already has an element equal to the one you are about to create, you can reuse
the existing instance. As a result of this arrangement, any two instances that are equal with
equals() are also equal with reference equality.
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252
Section 11.4
Chapter 11 · Scala’s Hierarchy
scala> val i: Int = null
:7: error: an expression of type Null is ineligible
for implicit conversion
val i: Int = null
ˆ
Type Nothing is at the very bottom of Scala’s class hierarchy; it is a subtype of every other type. However, there exist no values of this type whatsoever. Why does it make sense to have a type without values? As discussed
in Section 7.4, one use of Nothing is that it signals abnormal termination.
For instance there’s the error method in the Predef object of Scala’s
standard library, which is defined like this:
def error(message: String): Nothing =
throw new RuntimeException(message)
The return type of error is Nothing, which tells users that the method will
not return normally (it throws an exception instead). Because Nothing is a
subtype of every other type, you can use methods like error in very flexible
ways. For instance:
def divide(x: Int, y: Int): Int =
if (y != 0) x / y
else error("can't divide by zero")
The “then” branch of the conditional, x / y, has type Int, whereas the else
branch, the call to error, has type Nothing. Because Nothing is a subtype
of Int, the type of the whole conditional is Int, as required.
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11.4
Defining your own value classes
As mentioned in Section 11.1, you can define your own value classes to
augment the ones that are built in. Like the built-in value classes, an instance
of your value class will usually compile to Java bytecode that does not use the
wrapper class. In contexts where a wrapper is needed, such as with generic
code, the value will get boxed and unboxed automatically.
Only certain classes can be made into value classes. For a class to be
a value class, it must have exactly one parameter and it must have nothing
inside it except defs. Furthermore, no other class can extend a value class,
and a value class cannot redefine equals or hashCode.
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253
Section 11.4
Chapter 11 · Scala’s Hierarchy
To define a value class, make it a subclass of AnyVal, and put val before
the one parameter. Here is an example value class:
class Dollars(val amount: Int) extends AnyVal {
override def toString() = "$" + amount
}
As described in Section 10.6, the val prefix allows the amount parameter to
be accessed as a field. For example, the following code creates an instance
of the value class, then retrieves the amount from it:
scala> val money = new Dollars(1000000)
money: Dollars = $1000000
scala> money.amount
res16: Int = 1000000
In this example, money refers to an instance of the value class. It is of type
Dollars in Scala source code, but the compiled Java bytecode will use type
Int directly.
This example defines a toString method, and the compiler figures out
when to use it. That’s why printing money gives $1000000, with a dollar
sign, but printing money.amount gives 1000000. You can even define multiple value types that are all backed by the same Int value. For example:
class SwissFrancs(val amount: Int) extends AnyVal {
override def toString() = amount + " CHF"
}
Even though both Dollars and SwissFrancs are represented as integers, it
works fine to use them in the same scope:
scala> val dollars = new Dollars(1000)
dollars: Dollars = $1000
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scala> val francs = new SwissFrancs(1000)
francs: SwissFrancs = 1000 CHF
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254
Section 11.4
Chapter 11 · Scala’s Hierarchy
Avoiding a types monoculture
To get the most benefit from the Scala class hierarchy, try to define a new
class for each domain concept, even when it would be possible to reuse the
same class for different purposes. Even if such a class is a so-called tiny type
with no methods or fields, defining the additional class is a way to help the
compiler be helpful to you.
For example, suppose you are writing some code to generate HTML. In
HTML, a style name is represented as a string. So are anchor identifiers.
HTML itself is also a string, so if you wanted, you could define helper code
using strings to represent all of these things, like this:
def title(text: String, anchor: String, style: String): String =
s"$text
"
That type signature has four strings in it! Such stringly typed code is technically strongly typed, but since everything in sight is of type String, the
compiler cannot help you detect the use of one when you meant to write the
other. For example, it won’t stop you from this travesty:
scala> title("chap:vcls", "bold", "Value Classes")
res17: String = chap:vcls
This HTML is mangled. The intended display text “Value Classes” is being
used as a style class, and the text being displayed is “chap.vcls,” which was
supposed to be an anchor. To top it off, the actual anchor identifier is “bold,”
which is supposed to be a style class. Despite this comedy of errors, the
compiler utters not a peep.
The compiler can be more helpful if you define a tiny type for each domain concept. For example, you could define a small class for styles, anchor
identifiers, display text, and HTML. Since these classes have one parameter
and no members, they can be defined as value classes:
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class Anchor(val value: String) extends AnyVal
class Style(val value: String) extends AnyVal
class Text(val value: String) extends AnyVal
class Html(val value: String) extends AnyVal
Given these classes, it is possible to write a version of title that has a less
trivial type signature:
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255
Section 11.5
Chapter 11 · Scala’s Hierarchy
def title(text: Text, anchor: Anchor, style: Style): Html =
new Html(
s"" +
s"" +
text.value +
"
"
)
If you try to use this version with the arguments in the wrong order, the
compiler can now detect the error. For example:
scala> title(new Anchor("chap:vcls"), new Style("bold"),
new Text("Value Classes"))
:18: error: type mismatch;
found
: Anchor
required: Text
new Anchor("chap:vcls"),
ˆ
:19: error: type mismatch;
found
: Style
required: Anchor
new Style("bold"),
ˆ
:20: error: type mismatch;
found
: Text
required: Style
new Text("Value Classes"))
ˆ
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11.5
Conclusion
In this chapter we showed you the classes at the top and bottom of Scala’s
class hierarchy. Now that you’ve gotten a good foundation on class inheritance in Scala, you’re ready to understand mixin composition. In the next
chapter, you’ll learn about traits.
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256
Chapter 12
Traits
Traits are a fundamental unit of code reuse in Scala. A trait encapsulates
method and field definitions, which can then be reused by mixing them into
classes. Unlike class inheritance, in which each class must inherit from just
one superclass, a class can mix in any number of traits. This chapter shows
you how traits work and shows two of the most common ways they are useful: widening thin interfaces to rich ones, and defining stackable modifications. It also shows how to use the Ordered trait and compares traits to the
multiple inheritance of other languages.
12.1
How traits work
A trait definition looks just like a class definition except that it uses the keyword trait. An example is shown in Listing 12.1:
trait Philosophical {
def philosophize() = {
println("I consume memory, therefore I am!")
}
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}
Listing 12.1 · The definition of trait Philosophical.
This trait is named Philosophical. It does not declare a superclass, so
like a class, it has the default superclass of AnyRef. It defines one method,
named philosophize, which is concrete. It’s a simple trait, just enough to
show how traits work.
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Section 12.1
Chapter 12 · Traits
Once a trait is defined, it can be mixed in to a class using either the
extends or with keywords. Scala programmers “mix in” traits rather than
inherit from them, because mixing in a trait has important differences from
the multiple inheritance found in many other languages. This issue is discussed in Section 12.6. For example, Listing 12.2 shows a class that mixes
in the Philosophical trait using extends:
class Frog extends Philosophical {
override def toString = "green"
}
Listing 12.2 · Mixing in a trait using extends.
You can use the extends keyword to mix in a trait; in that case you
implicitly inherit the trait’s superclass. For instance, in Listing 12.2, class
Frog subclasses AnyRef (the superclass of Philosophical) and mixes in
Philosophical. Methods inherited from a trait can be used just like methods inherited from a superclass. Here’s an example:
scala> val frog = new Frog
frog: Frog = green
scala> frog.philosophize()
I consume memory, therefore I am!
A trait also defines a type. Here’s an example in which Philosophical is
used as a type:
scala> val phil: Philosophical = frog
phil: Philosophical = green
scala> phil.philosophize()
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I consume memory, therefore I am!
The type of phil is Philosophical, a trait. Thus, variable phil could have
been initialized with any object whose class mixes in Philosophical.
If you wish to mix a trait into a class that explicitly extends a superclass,
you use extends to indicate the superclass and with to mix in the trait.
Listing 12.3 shows an example. If you want to mix in multiple traits, you
add more with clauses. For example, given a trait HasLegs, you could mix
both Philosophical and HasLegs into Frog as shown in Listing 12.4.
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258
Section 12.1
Chapter 12 · Traits
class Animal
class Frog extends Animal with Philosophical {
override def toString = "green"
}
Listing 12.3 · Mixing in a trait using with.
class Animal
trait HasLegs
class Frog extends Animal with Philosophical with HasLegs {
override def toString = "green"
}
Listing 12.4 · Mixing in multiple traits.
In the examples you’ve seen so far, class Frog has inherited an implementation of philosophize from trait Philosophical. Alternatively, Frog
could override philosophize. The syntax looks the same as overriding a
method declared in a superclass. Here’s an example:
class Animal
class Frog extends Animal with Philosophical {
override def toString = "green"
override def philosophize() = {
println("It ain't easy being " + toString + "!")
}
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}
Because this new definition of Frog still mixes in trait Philosophical, you
can still use it from a variable of that type. But because Frog overrides
Philosophical’s implementation of philosophize, you’ll get a new behavior when you call it:
scala> val phrog: Philosophical = new Frog
phrog: Philosophical = green
scala> phrog.philosophize()
It ain't easy being green!
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259
Section 12.2
Chapter 12 · Traits
At this point you might philosophize that traits are like Java interfaces
with concrete methods, but they can actually do much more. Traits can, for
example, declare fields and maintain state. In fact, you can do anything in
a trait definition that you can do in a class definition, and the syntax looks
exactly the same, with only two exceptions.
First, a trait cannot have any “class” parameters (i.e., parameters passed
to the primary constructor of a class). In other words, although you could
define a class like this:
class Point(x: Int, y: Int)
The following attempt to define a trait would not compile:
trait NoPoint(x: Int, y: Int) // Does not compile
You’ll find out in Section 20.5 how to work around this restriction.
The other difference between classes and traits is that whereas in classes,
super calls are statically bound, in traits, they are dynamically bound. If
you write “super.toString” in a class, you know exactly which method
implementation will be invoked. When you write the same thing in a trait,
however, the method implementation to invoke for the super call is undefined when you define the trait. Rather, the implementation to invoke will
be determined anew each time the trait is mixed into a concrete class. This
curious behavior of super is key to allowing traits to work as stackable modifications, which will be described in Section 12.5. The rules for resolving
super calls will be given in Section 12.6.
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12.2
Thin versus rich interfaces
One major use of traits is to automatically add methods to a class in terms
of methods the class already has. That is, traits can enrich a thin interface,
making it into a rich interface.
Thin versus rich interfaces represents a commonly faced trade-off in
object-oriented design. The trade-off is between the implementers and the
clients of an interface. A rich interface has many methods, which make it
convenient for the caller. Clients can pick a method that exactly matches
the functionality they need. A thin interface, on the other hand, has fewer
methods, and thus is easier on the implementers. Clients calling into a thin
interface, however, have to write more code. Given the smaller selection of
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260
Section 12.3
Chapter 12 · Traits
methods to call, they may have to choose a less than perfect match for their
needs and write extra code to use it.
Java’s interfaces are more often thin than rich. For example, interface
CharSequence, which was introduced in Java 1.4, is a thin interface common to all string-like classes that hold a sequence of characters. Here’s its
definition when seen as a Scala trait:
trait CharSequence {
def charAt(index: Int): Char
def length: Int
def subSequence(start: Int, end: Int): CharSequence
def toString(): String
}
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Although most of the dozens of methods in class String would apply to
any CharSequence, Java’s CharSequence interface declares only four methods. Had CharSequence instead included the full String interface, it would
have placed a large burden on implementers of CharSequence. Every programmer that implemented CharSequence in Java would have had to define
dozens more methods. Because Scala traits can contain concrete methods,
they make rich interfaces far more convenient.
Adding a concrete method to a trait tilts the thin-rich trade-off heavily
towards rich interfaces. Unlike in Java, adding a concrete method to a Scala
trait is a one-time effort. You only need to implement the method once, in
the trait itself, instead of needing to reimplement it for every class that mixes
in the trait. Thus, rich interfaces are less work to provide in Scala than in a
language without traits.
To enrich an interface using traits, simply define a trait with a small number of abstract methods—the thin part of the trait’s interface—and a potentially large number of concrete methods, all implemented in terms of the
abstract methods. Then you can mix the enrichment trait into a class, implement the thin portion of the interface, and end up with a class that has all of
the rich interface available.
12.3
Example: Rectangular objects
Graphics libraries often have many different classes that represent something
rectangular. Some examples are windows, bitmap images, and regions seCover · Overview · Contents · Discuss · Suggest · Glossary · Index
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lected with a mouse. To make these rectangular objects convenient to use,
it is nice if the library provides geometric queries, such as width, height,
left, right, topLeft, and so on. However, many such methods exist that
would be nice to have, so it can be a large burden on library writers to provide all of them for all rectangular objects in a Java library. If such a library
were written in Scala, by contrast, the library writer could use traits to easily
supply all of these convenience methods on all the classes they like.
To see how, first imagine what the code would look like without traits.
There would be some basic geometric classes like Point and Rectangle:
class Point(val x: Int, val y: Int)
class Rectangle(val topLeft: Point, val bottomRight: Point) {
def left = topLeft.x
def right = bottomRight.x
def width = right - left
// and many more geometric methods...
}
This Rectangle class takes two points in its primary constructor: the coordinates of the top-left and bottom-right corners. It then implements many
convenience methods, such as left, right, and width, by performing simple calculations on these two points.
Another class a graphics library might have is a 2-D graphical widget:
abstract class Component {
def topLeft: Point
def bottomRight: Point
def left = topLeft.x
def right = bottomRight.x
def width = right - left
// and many more geometric methods...
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}
Notice that the definitions of left, right, and width are exactly the same
in the two classes. They will also be the same, aside from minor variations,
in any other classes for rectangular objects.
This repetition can be eliminated with an enrichment trait. The trait will
have two abstract methods: one that returns the top-left coordinate of the object, and another that returns the bottom-right coordinate. It can then supply
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concrete implementations of all the other geometric queries. Listing 12.5
shows what it will look like:
trait Rectangular {
def topLeft: Point
def bottomRight: Point
def left = topLeft.x
def right = bottomRight.x
def width = right - left
// and many more geometric methods...
}
Listing 12.5 · Defining an enrichment trait.
Class Component can mix in this trait to get all the geometric methods
provided by Rectangular:
abstract class Component extends Rectangular {
// other methods...
}
Similarly, Rectangle itself can mix in the trait:
class Rectangle(val topLeft: Point, val bottomRight: Point)
extends Rectangular {
// other methods...
}
Given these definitions, you can create a Rectangle and call geometric
methods such as width and left on it:
scala> val rect = new Rectangle(new Point(1, 1),
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new Point(10, 10))
rect: Rectangle = Rectangle@5f5da68c
scala> rect.left
res2: Int = 1
scala> rect.right
res3: Int = 10
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scala> rect.width
res4: Int = 9
12.4
The Ordered trait
Comparison is another domain where a rich interface is convenient. Whenever you compare two objects that are ordered, it is convenient if you use
a single method call to ask about the precise comparison you want. If you
want “is less than,” you would like to call <, and if you want “is less than
or equal,” you would like to call <=. With a thin comparison interface, you
might just have the < method, and you would sometimes have to write things
like “(x < y) || (x == y)”. A rich interface would provide you with all of
the usual comparison operators, thus allowing you to directly write things
like “x <= y”.
Before looking at Ordered, imagine what you might do without it. Suppose you took the Rational class from Chapter 6 and added comparison
operations to it. You would end up with something like this:1
class Rational(n: Int, d: Int) {
// ...
def < (that: Rational) =
this.numer * that.denom < that.numer * this.denom
def > (that: Rational) = that < this
def <= (that: Rational) = (this < that) || (this == that)
def >= (that: Rational) = (this > that) || (this == that)
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}
This class defines four comparison operators (<, >, <=, and >=), and it’s a
classic demonstration of the costs of defining a rich interface. First, notice
that three of the comparison operators are defined in terms of the first one.
For example, > is defined as the reverse of <, and <= is defined as literally
“less than or equal.” Next, notice that all three of these methods would be the
same for any other class that is comparable. There is nothing special about
rational numbers regarding <=. In a comparison context, <= is always used
to mean “less than or equals.” Overall, there is quite a lot of boilerplate code
1 This example is based on the Rational class shown in Listing 6.5 on page 151, with
equals, hashCode, and modifications to ensure a positive denom added.
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in this class which would be the same in any other class that implements
comparison operations.
This problem is so common that Scala provides a trait to help with it. The
trait is called Ordered. To use it, you replace all of the individual comparison
methods with a single compare method. The Ordered trait then defines <, >,
<=, and >= for you in terms of this one method. Thus, trait Ordered allows
you to enrich a class with comparison methods by implementing only one
method, compare.
Here is how it looks if you define comparison operations on Rational
by using the Ordered trait:
class Rational(n: Int, d: Int) extends Ordered[Rational] {
// ...
def compare(that: Rational) =
(this.numer * that.denom) - (that.numer * this.denom)
}
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There are just two things to do. First, this version of Rational mixes in the
Ordered trait. Unlike the traits you have seen so far, Ordered requires you
to specify a type parameter when you mix it in. Type parameters are not
discussed in detail until Chapter 19, but for now all you need to know is that
when you mix in Ordered, you must actually mix in Ordered[C], where C
is the class whose elements you compare. In this case, Rational mixes in
Ordered[Rational].
The second thing you need to do is define a compare method for comparing two objects. This method should compare the receiver, this, with
the object passed as an argument to the method. It should return an integer
that is zero if the objects are the same, negative if receiver is less than the
argument, and positive if the receiver is greater than the argument.
In this case, the comparison method of Rational uses a formula based
on converting the fractions to a common denominator and then subtracting
the resulting numerators. Given this mixin and the definition of compare,
class Rational now has all four comparison methods:
scala> val half = new Rational(1, 2)
half: Rational = 1/2
scala> val third = new Rational(1, 3)
third: Rational = 1/3
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scala> half < third
res5: Boolean = false
scala> half > third
res6: Boolean = true
Any time you implement a class that is ordered by some comparison,
you should consider mixing in the Ordered trait. If you do, you will provide
the class’s users with a rich set of comparison methods.
Beware that the Ordered trait does not define equals for you, because
it is unable to do so. The problem is that implementing equals in terms of
compare requires checking the type of the passed object, and because of type
erasure, Ordered itself cannot do this test. Thus, you need to define equals
yourself, even if you inherit Ordered. You’ll find out how to go about this
in Chapter 30.
12.5
Traits as stackable modifications
You have now seen one major use of traits: turning a thin interface into a
rich one. Now we’ll turn to a second major use: providing stackable modifications to classes. Traits let you modify the methods of a class, and they do
so in a way that allows you to stack those modifications with each other.
As an example, consider stacking modifications to a queue of integers.
The queue will have two operations: put, which places integers in the queue,
and get, which takes them back out. Queues are first-in, first-out, so get
should return the integers in the same order they were put in the queue.
Given a class that implements such a queue, you could define traits to
perform modifications such as these:
• Doubling: double all integers that are put in the queue
• Incrementing: increment all integers that are put in the queue
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• Filtering: filter out negative integers from a queue
These three traits represent modifications, because they modify the behavior of an underlying queue class rather than defining a full queue class
themselves. The three are also stackable. You can select any of the three
you like, mix them into a class, and obtain a new class that has all of the
modifications you chose.
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An abstract IntQueue class is shown in Listing 12.6. IntQueue has
a put method that adds new integers to the queue, and a get method that
removes and returns them. A basic implementation of IntQueue that uses
an ArrayBuffer is shown in Listing 12.7.
abstract class IntQueue {
def get(): Int
def put(x: Int)
}
Listing 12.6 · Abstract class IntQueue.
import scala.collection.mutable.ArrayBuffer
class BasicIntQueue extends IntQueue {
private val buf = new ArrayBuffer[Int]
def get() = buf.remove(0)
def put(x: Int) = { buf += x }
}
Listing 12.7 · A BasicIntQueue implemented with an ArrayBuffer.
Class BasicIntQueue has a private field holding an array buffer. The
get method removes an entry from one end of the buffer, while the put
method adds elements to the other end. Here’s how this implementation
looks when you use it:
scala> val queue = new BasicIntQueue
queue: BasicIntQueue = BasicIntQueue@23164256
scala> queue.put(10)
scala> queue.put(20)
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scala> queue.get()
res9: Int = 10
scala> queue.get()
res10: Int = 20
So far so good. Now take a look at using traits to modify this behavior.
Listing 12.8 shows a trait that doubles integers as they are put in the queue.
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The Doubling trait has two funny things going on. The first is that it declares
a superclass, IntQueue. This declaration means that the trait can only be
mixed into a class that also extends IntQueue. Thus, you can mix Doubling
into BasicIntQueue, but not into Rational.
trait Doubling extends IntQueue {
abstract override def put(x: Int) = { super.put(2 * x) }
}
Listing 12.8 · The Doubling stackable modification trait.
The second funny thing is that the trait has a super call on a method
declared abstract. Such calls are illegal for normal classes because they will
certainly fail at run time. For a trait, however, such a call can actually succeed. Since super calls in a trait are dynamically bound, the super call in
trait Doubling will work so long as the trait is mixed in after another trait or
class that gives a concrete definition to the method.
This arrangement is frequently needed with traits that implement stackable modifications. To tell the compiler you are doing this on purpose, you
must mark such methods as abstract override. This combination of modifiers is only allowed for members of traits, not classes, and it means that
the trait must be mixed into some class that has a concrete definition of the
method in question.
There is a lot going on with such a simple trait, isn’t there! Here’s how
it looks to use the trait:
scala> class MyQueue extends BasicIntQueue with Doubling
defined class MyQueue
scala> val queue = new MyQueue
queue: MyQueue = MyQueue@44bbf788
scala> queue.put(10)
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scala> queue.get()
res12: Int = 20
In the first line in this interpreter session, we define class MyQueue, which
extends BasicIntQueue and mixes in Doubling. We then put a 10 in the
queue, but because Doubling has been mixed in, the 10 is doubled. When
we get an integer from the queue, it is a 20.
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Note that MyQueue defines no new code. It simply identifies a class and
mixes in a trait. In this situation, you could supply “BasicIntQueue with
Doubling” directly to new instead of defining a named class. It would look
as shown in Listing 12.9:
scala> val queue = new BasicIntQueue with Doubling
queue: BasicIntQueue with Doubling = $anon$1@141f05bf
scala> queue.put(10)
scala> queue.get()
res14: Int = 20
Listing 12.9 · Mixing in a trait when instantiating with new.
To see how to stack modifications, we need to define the other two modification traits, Incrementing and Filtering. Implementations of these
traits are shown in Listing 12.10:
trait Incrementing extends IntQueue {
abstract override def put(x: Int) = { super.put(x + 1) }
}
trait Filtering extends IntQueue {
abstract override def put(x: Int) = {
if (x >= 0) super.put(x)
}
}
Listing 12.10: Stackable modification traits Incrementing and Filtering.
Given these modifications, you can now pick and choose which ones you
want for a particular queue. For example, here is a queue that both filters
negative numbers and adds one to all numbers that it keeps:
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scala> val queue = (new BasicIntQueue
with Incrementing with Filtering)
queue: BasicIntQueue with Incrementing with Filtering...
scala> queue.put(-1); queue.put(0); queue.put(1)
scala> queue.get()
res16: Int = 1
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scala> queue.get()
res17: Int = 2
The order of mixins is significant.2 The precise rules are given in the
following section, but, roughly speaking, traits further to the right take effect
first. When you call a method on a class with mixins, the method in the
trait furthest to the right is called first. If that method calls super, it invokes
the method in the next trait to its left, and so on. In the previous example,
Filtering’s put is invoked first, so it removes integers that were negative to
begin with. Incrementing’s put is invoked second, so it adds one to those
integers that remain.
If you reverse the order, first integers will be incremented, and then the
integers that are still negative will be discarded:
scala> val queue = (new BasicIntQueue
with Filtering with Incrementing)
queue: BasicIntQueue with Filtering with Incrementing...
scala> queue.put(-1); queue.put(0); queue.put(1)
scala> queue.get()
res19: Int = 0
scala> queue.get()
res20: Int = 1
scala> queue.get()
res21: Int = 2
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Overall, code written in this style gives you a great deal of flexibility. You
can define sixteen different classes by mixing in these three traits in different
combinations and orders. That’s a lot of flexibility for a small amount of
code, so you should keep your eyes open for opportunities to arrange code
as stackable modifications.
12.6
Why not multiple inheritance?
Traits are a way to inherit from multiple class-like constructs, but they differ
in important ways from the multiple inheritance present in many languages.
2 Once
a trait is mixed into a class, you can alternatively call it a mixin.
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One difference is especially important: the interpretation of super. With
multiple inheritance, the method called by a super call can be determined
right where the call appears. With traits, the method called is determined
by a linearization of the classes and traits that are mixed into a class. This
is the difference that enables the stacking of modifications described in the
previous section.
Before looking at linearization, take a moment to consider how to stack
modifications in a language with traditional multiple inheritance. Imagine
the following code, but this time interpreted as multiple inheritance instead
of trait mixin:
// Multiple inheritance thought experiment
val q = new BasicIntQueue with Incrementing with Doubling
q.put(42)
// which put would be called?
The first question is: Which put method would get invoked by this call? Perhaps the rule would be that the last superclass wins, in which case Doubling
would get called. Doubling would double its argument and call super.put,
and that would be it. No incrementing would happen! Likewise, if the rule
were that the first superclass wins, the resulting queue would increment integers but not double them. Thus neither ordering would work.
You might also entertain the possibility of allowing programmers to identify exactly which superclass method they want when they say super. For
example, imagine the following Scala-like code, in which super appears to
be explicitly invoked on both Incrementing and Doubling:
// Multiple inheritance thought experiment
trait MyQueue extends BasicIntQueue
with Incrementing with Doubling {
def put(x: Int) = {
Incrementing.super.put(x) // (Not real Scala)
Doubling.super.put(x)
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}
}
This approach would give us new problems (with the verbosity of this
attempt being the least of its problems). What would happen is that the
base class’s put method would get called twice—once with an incremented
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value and once with a doubled value, but neither time with an incremented,
doubled value.
There is simply no good solution to this problem using multiple inheritance. You would have to back up in your design and factor the code differently. By contrast, the traits solution in Scala is straightforward. You
simply mix in Incrementing and Doubling, and Scala’s special treatment
of super in traits makes it all work out. Something is clearly different here
from traditional multiple inheritance, but what?
As hinted previously, the answer is linearization. When you instantiate a
class with new, Scala takes the class, and all of its inherited classes and traits,
and puts them in a single, linear order. Then, whenever you call super inside
one of those classes, the invoked method is the next one up the chain. If all
of the methods but the last call super, the net result is stackable behavior.
The precise order of the linearization is described in the language specification. It is a little bit complicated, but the main thing you need to know
is that, in any linearization, a class is always linearized in front of all its
superclasses and mixed in traits. Thus, when you write a method that calls
super, that method is definitely modifying the behavior of the superclasses
and mixed in traits, not the other way around.
Note
The remainder of this section describes the details of linearization. You
can safely skip the rest of this section if you are not interested in
understanding those details right now.
The main properties of Scala’s linearization are illustrated by the following example: Say you have a class Cat, which inherits from a superclass
Animal and two supertraits Furry and FourLegged. FourLegged extends
in turn another trait HasLegs:
class Animal
trait Furry extends Animal
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trait HasLegs extends Animal
trait FourLegged extends HasLegs
class Cat extends Animal with Furry with FourLegged
Class Cat’s inheritance hierarchy and linearization are shown in Figure 12.1. Inheritance is indicated using traditional UML notation:3 arrows
3 Rumbaugh,
et al., The Unified Modeling Language Reference Manual. [Rum04]
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273
Any
AnyRef
Animal
HasLegs
Furry
FourLegged
Cat
Figure 12.1 · Inheritance hierarchy and linearization of class Cat.
with white, triangular arrowheads indicate inheritance, with the arrowhead
pointing to the supertype. The arrows with darkened, non-triangular arrowheads depict linearization. The darkened arrowheads point in the direction
in which super calls will be resolved.
The linearization of Cat is computed from back to front as follows. The
last part of the linearization of Cat is the linearization of its superclass,
Animal. This linearization is copied over without any changes. (The linearization of each of these types is shown in Table 12.1 on page 274.) Because Animal doesn’t explicitly extend a superclass or mix in any supertraits,
it by default extends AnyRef, which extends Any. Animal’s linearization,
therefore, looks like:
Animal
AnyRef
Any
The second to last part is the linearization of the first mixin, trait Furry, but
all classes that are already in the linearization of Animal are left out now, so
that each class appears only once in Cat’s linearization. The result is:
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Furry
Animal
AnyRef
Any
This is preceded by the linearization of FourLegged, where again any classes
that have already been copied in the linearizations of the superclass or the
first mixin are left out:
FourLegged
HasLegs
Furry
Animal
AnyRef
Any
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274
Table 12.1 · Linearization of types in Cat’s hierarchy
Type
Animal
Furry
FourLegged
HasLegs
Cat
Linearization
Animal, AnyRef, Any
Furry, Animal, AnyRef, Any
FourLegged, HasLegs, Animal, AnyRef, Any
HasLegs, Animal, AnyRef, Any
Cat, FourLegged, HasLegs, Furry, Animal, AnyRef, Any
Finally, the first class in the linearization of Cat is Cat itself:
Cat
FourLegged
HasLegs
Furry
Animal
AnyRef
Any
When any of these classes and traits invokes a method via super, the implementation invoked will be the first implementation to its right in the linearization.
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12.7
To trait or not to trait?
Whenever you implement a reusable collection of behavior, you will have to
decide whether you want to use a trait or an abstract class. There is no firm
rule, but this section contains a few guidelines to consider.
If the behavior will not be reused, then make it a concrete class. It is not
reusable behavior after all.
If it might be reused in multiple, unrelated classes, make it a trait. Only
traits can be mixed into different parts of the class hierarchy.
If you want to inherit from it in Java code, use an abstract class. Since
traits with code do not have a close Java analog, it tends to be awkward to
inherit from a trait in a Java class. Inheriting from a Scala class, meanwhile,
is exactly like inheriting from a Java class. As one exception, a Scala trait
with only abstract members translates directly to a Java interface, so you
should feel free to define such traits even if you expect Java code to inherit
from it. See Chapter 31 for more information on working with Java and
Scala together.
If you plan to distribute it in compiled form, and you expect outside
groups to write classes inheriting from it, you might lean towards using an
abstract class. The issue is that when a trait gains or loses a member, any
classes that inherit from it must be recompiled, even if they have not changed.
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If outside clients will only call into the behavior, instead of inheriting from
it, then using a trait is fine.
If you still do not know, after considering the above, then start by making
it as a trait. You can always change it later, and in general using a trait keeps
more options open.
12.8
Conclusion
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This chapter has shown you how traits work and how to use them in several
common idioms. You saw that traits are similar to multiple inheritance. But
because traits interpret super using linearization, they both avoid some of
the difficulties of traditional multiple inheritance and allow you to stack behaviors. You also saw the Ordered trait and learned how to write your own
enrichment traits.
Now that you have seen all of these facets, it is worth stepping back and
taking another look at traits as a whole. Traits do not merely support the
idioms described in this chapter; they are a fundamental unit of code that
is reusable through inheritance. As such, many experienced Scala programmers start with traits when they are at the early stages of implementation.
Each trait can hold less than an entire concept, a mere fragment of a concept.
As the design solidifies, the fragments can be combined into more complete
concepts through trait mixin.
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Chapter 13
Packages and Imports
When working on a program, especially a large one, it is important to minimize coupling—the extent to which the various parts of the program rely
on the other parts. Low coupling reduces the risk that a small, seemingly
innocuous change in one part of the program will have devastating consequences in another part. One way to minimize coupling is to write in a
modular style. You divide the program into a number of smaller modules,
each of which has an inside and an outside. When working on the inside
of a module—its implementation—you need only coordinate with other programmers working on that very same module. Only when you must change
the outside of a module—its interface—is it necessary to coordinate with
developers working on other modules.
This chapter shows several constructs that help you program in a modular
style. It shows how to place things in packages, make names visible through
imports, and control the visibility of definitions through access modifiers.
The constructs are similar in spirit to constructs in Java, but there are some
differences—usually ways that are more consistent—so it’s worth reading
this chapter even if you already know Java.
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13.1
Putting code in packages
Scala code resides in the Java platform’s global hierarchy of packages. The
example code you’ve seen so far in this book has been in the unnamed
package. You can place code into named packages in Scala in two ways.
First, you can place the contents of an entire file into a package by putting a
package clause at the top of the file, as shown in Listing 13.1.
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Section 13.2
Chapter 13 · Packages and Imports
package bobsrockets.navigation
class Navigator
Listing 13.1 · Placing the contents of an entire file into a package.
The package clause of Listing 13.1 places class Navigator into the
package named bobsrockets.navigation. Presumably, this is the navigation software developed by Bob’s Rockets, Inc.
Note
Because Scala code is part of the Java ecosystem, it is recommended to
follow Java’s reverse-domain-name convention for Scala packages that
you release to the public. Thus, a better name for Navigator’s package
might be com.bobsrockets.navigation. In this chapter, however, we’ll
leave off the “com.” to make the examples easier to understand.
The other way you can place code into packages in Scala is more like
C# namespaces. You follow a package clause by a section in curly braces
that contains the definitions that go into the package. This syntax is called a
packaging. The packaging shown in Listing 13.2 has the same effect as the
code in Listing 13.1:
package bobsrockets.navigation {
class Navigator
}
Listing 13.2 · Long form of a simple package declaration.
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For such simple examples, you might as well use the syntactic sugar
shown in Listing 13.1. However, one use of the more general notation is to
have different parts of a file in different packages. For example, you might
include a class’s tests in the same file as the original code, but put the tests
in a different package, as shown in Listing 13.3.
13.2
Concise access to related code
When code is divided into a package hierarchy, it doesn’t just help people
browse through the code. It also tells the compiler that code in the same
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package bobsrockets {
package navigation {
// In package bobsrockets.navigation
class Navigator
package tests {
// In package bobsrockets.navigation.tests
class NavigatorSuite
}
}
}
Listing 13.3 · Multiple packages in the same file.
package bobsrockets {
package navigation {
class Navigator {
// No need to say bobsrockets.navigation.StarMap
val map = new StarMap
}
class StarMap
}
class Ship {
// No need to say bobsrockets.navigation.Navigator
val nav = new navigation.Navigator
}
package fleets {
class Fleet {
// No need to say bobsrockets.Ship
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def addShip() = { new Ship }
}
}
}
Listing 13.4 · Concise access to classes and packages.
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package bobsrockets {
class Ship
}
package bobsrockets.fleets {
class Fleet {
// Doesn't compile! Ship is not in scope.
def addShip() = { new Ship }
}
}
Listing 13.5 · Symbols in enclosing packages not automatically available.
// In file launch.scala
package launch {
class Booster3
}
// In file bobsrockets.scala
package bobsrockets {
package navigation {
package launch {
class Booster1
}
class MissionControl {
val booster1 = new launch.Booster1
val booster2 = new bobsrockets.launch.Booster2
val booster3 = new _root_.launch.Booster3
}
}
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package launch {
class Booster2
}
}
Listing 13.6 · Accessing hidden package names.
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package is related in some way to each other. Scala takes advantage of this
relatedness by allowing short, unqualified names when accessing code that
is in the same package.
Listing 13.4 gives three simple examples. First, as you would expect, a
class can be accessed from within its own package without needing a prefix.
That’s why new StarMap compiles. Class StarMap is in the same package,
bobsrockets.navigation, as the new expression that accesses it, so the
package name doesn’t need to be prefixed.
Second, a package itself can be accessed from its containing package
without needing a prefix. In Listing 13.4, look at how class Navigator is
instantiated. The new expression appears in package bobsrockets, which is
the containing package of bobsrockets.navigation. Thus, it can access
package bobsrockets.navigation as simply navigation.
Third, when using the curly-braces packaging syntax, all names accessible in scopes outside the packaging are also available inside it. An example
in Listing 13.4 is the way addShip() creates a new Ship. The method is
defined within two packagings: an outer one for bobsrockets, and an inner one for bobsrockets.fleets. Since Ship is accessible in the outer
packaging, it can be referenced from within addShip().
Note that this kind of access is only available if you explicitly nest the
packagings. If you stick to one package per file, then—like in Java—the
only names available will be the ones defined in the current package. In Listing 13.5, the packaging of bobsrockets.fleets has been moved to the top
level. Since it is no longer enclosed in a packaging for bobsrockets, names
from bobsrockets are not immediately in scope. As a result, new Ship gives
a compile error. If nesting packages with braces shifts your code uncomfortably to the right, you can also use multiple package clauses without the
braces.1 For instance, the code below also defines class Fleet in two nested
packages bobrockets and fleets, just like you saw it in Listing 13.4:
package bobsrockets
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package fleets
class Fleet {
// No need to say bobsrockets.Ship
def addShip() = { new Ship }
}
1 This style of multiple package clauses without braces is called chained package clauses.
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One final trick is important to know. Sometimes, you end up coding in a
heavily crowded scope where package names are hiding each other. In Listing 13.6, the scope of class MissionControl includes three separate packages named launch! There’s one launch in bobsrockets.navigation,
one in bobsrockets, and one at the top level. How would you reference
each of Booster1, Booster2, and Booster3?
Accessing the first one is easiest. A reference to launch by itself will
get you to package bobsrockets.navigation.launch, because that is the
launch package defined in the closest enclosing scope. Thus, you can refer
to the first booster class as simply launch.Booster1. Referring to the second one also is not tricky. You can write bobrockets.launch.Booster2
and be clear about which one you are referencing. That leaves the question
of the third booster class: How can you access Booster3, considering that a
nested launch package shadows the top-level one?
To help in this situation, Scala provides a package named _root_ that
is outside any package a user can write. Put another way, every top-level
package you can write is treated as a member of package _root_. For example, both launch and bobsrockets of Listing 13.6 are members of package
_root_. As a result, _root_.launch gives you the top-level launch package, and _root_.launch.Booster3 designates the outermost booster class.
13.3
Imports
In Scala, packages and their members can be imported using import clauses.
Imported items can then be accessed by a simple name like File, as opposed
to requiring a qualified name like java.io.File. For example, consider the
code shown in Listing 13.7.
An import clause makes members of a package or object available by
their names alone without needing to prefix them by the package or object
name. Here are some simple examples:
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// easy access to Fruit
import bobsdelights.Fruit
// easy access to all members of bobsdelights
import bobsdelights._
// easy access to all members of Fruits
import bobsdelights.Fruits._
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package bobsdelights
abstract class Fruit(
val name: String,
val color: String
)
object Fruits {
object Apple extends Fruit("apple", "red")
object Orange extends Fruit("orange", "orange")
object Pear extends Fruit("pear", "yellowish")
val menu = List(Apple, Orange, Pear)
}
Listing 13.7 · Bob’s delightful fruits, ready for import.
The first of these corresponds to Java’s single type import and the second
to Java’s on-demand import. The only difference is that Scala’s on-demand
imports are written with a trailing underscore (_) instead of an asterisk (*).
(After all, * is a valid identifier in Scala!) The third import clause above
corresponds to Java’s import of static class fields.
These three imports give you a taste of what imports can do, but Scala
imports are actually much more general. For one, imports in Scala can appear anywhere, not just at the beginning of a compilation unit. Also, they
can refer to arbitrary values. For instance, the import shown in Listing 13.8
is possible:
def showFruit(fruit: Fruit) = {
import fruit._
println(name + "s are " + color)
}
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Listing 13.8 · Importing the members of a regular (not singleton) object.
Method showFruit imports all members of its parameter fruit, which
is of type Fruit. The subsequent println statement can refer to name and
color directly. These two references are equivalent to fruit.name and
fruit.color. This syntax is particularly useful when you use objects as
modules, which will be described in Chapter 29.
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Scala’s flexible imports
Scala’s import clauses are quite a bit more flexible than Java’s. There
are three principal differences. In Scala, imports:
• may appear anywhere
• may refer to objects (singleton or regular) in addition to packages
• let you rename and hide some of the imported members
Another way Scala’s imports are flexible is that they can import packages
themselves, not just their non-package members. This is only natural if you
think of nested packages being contained in their surrounding package. For
example, in Listing 13.9, the package java.util.regex is imported. This
makes regex usable as a simple name. To access the Pattern singleton object from the java.util.regex package, you can just say, regex.Pattern,
as shown in Listing 13.9:
import java.util.regex
class AStarB {
// Accesses java.util.regex.Pattern
val pat = regex.Pattern.compile("a*b")
}
Listing 13.9 · Importing a package name.
Imports in Scala can also rename or hide members. This is done with
an import selector clause enclosed in braces, which follows the object from
which members are imported. Here are some examples:
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import Fruits.{Apple, Orange}
This imports just members Apple and Orange from object Fruits.
import Fruits.{Apple => McIntosh, Orange}
This imports the two members Apple and Orange from object Fruits.
However, the Apple object is renamed to McIntosh, so this object can be
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accessed with either Fruits.Apple or McIntosh. A renaming clause is
always of the form “ => ”.
import java.sql.{Date => SDate}
This imports the SQL date class as SDate, so that you can simultaneously
import the normal Java date class as simply Date.
import java.{sql => S}
This imports the java.sql package as S, so that you can write things
like S.Date.
import Fruits.{_}
This imports all members from object Fruits. It means the same thing
as import Fruits._.
import Fruits.{Apple => McIntosh, _}
This imports all members from object Fruits but renames Apple to
McIntosh.
import Fruits.{Pear => _, _}
This imports all members of Fruits except Pear. A clause of the form
“ => _” excludes from the names that
are imported. In a sense, renaming something to ‘_’ means hiding it altogether. This is useful to avoid ambiguities. Say you have two packages,
Fruits and Notebooks, which both define a class Apple. If you want to
get just the notebook named Apple, and not the fruit, you could still use two
imports on demand like this:
import Notebooks._
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import Fruits.{Apple => _, _}
This would import all Notebooks and all Fruits, except for Apple.
These examples demonstrate the great flexibility Scala offers when it
comes to importing members selectively and possibly under different names.
In summary, an import selector can consist of the following:
• A simple name x. This includes x in the set of imported names.
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• A renaming clause x => y. This makes the member named x visible
under the name y.
• A hiding clause x => _. This excludes x from the set of imported
names.
• A catch-all ‘_’. This imports all members except those members mentioned in a preceding clause. If a catch-all is given, it must come last
in the list of import selectors.
The simpler import clauses shown at the beginning of this section can be
seen as special abbreviations of import clauses with a selector clause. For
example, “import p._” is equivalent to “import p.{_}” and “import p.n”
is equivalent to “import p.{n}”.
13.4
Implicit imports
Scala adds some imports implicitly to every program. In essence, it is as if
the following three import clauses had been added to the top of every source
file with extension “.scala”:
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import java.lang._ // everything in the java.lang package
import scala._
// everything in the scala package
import Predef._
// everything in the Predef object
The java.lang package contains standard Java classes. It is always implicitly imported in Scala source files.2 Because java.lang is imported implicitly, you can write Thread instead of java.lang.Thread, for instance.
As you have no doubt realized by now, the scala package contains the
standard Scala library, with many common classes and objects. Because
scala is imported implicitly, you can write List instead of scala.List,
for instance.
The Predef object contains many definitions of types, methods, and implicit conversions that are commonly used on Scala programs. For example, because Predef is imported implicitly, you can write assert instead of
Predef.assert.
2 Scala also originally had an implementation on .NET., where namespace System, the
.NET analogue of package java.lang, was imported instead.
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These three import clauses are treated a bit specially in that later imports overshadow earlier ones. For instance, the StringBuilder class is defined both in package scala and, from Java version 1.5 on, also in package
java.lang. Because the scala import overshadows the java.lang import,
the simple name StringBuilder will refer to scala.StringBuilder, not
java.lang.StringBuilder.
13.5
Access modifiers
Members of packages, classes, or objects can be labeled with the access
modifiers private and protected. These modifiers restrict access to the
members to certain regions of code. Scala’s treatment of access modifiers
roughly follows Java’s but there are some important differences which are
explained in this section.
Private members
Private members in Scala are treated similarly to Java. A member labeled
private is visible only inside the class or object that contains the member
definition. In Scala, this rule applies also for inner classes. This treatment
is more consistent, but differs from Java. Consider the example shown in
Listing 13.10.
class Outer {
class Inner {
private def f() = { println("f") }
class InnerMost {
f() // OK
}
}
(new Inner).f() // error: f is not accessible
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}
Listing 13.10 · How private access differs in Scala and Java.
In Scala, the access (new Inner).f() is illegal because f is declared
private in Inner and the access is not from within class Inner. By contrast, the first access to f in class InnerMost is OK, because that access
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is contained in the body of class Inner. Java would permit both accesses
because it lets an outer class access private members of its inner classes.
Protected members
Access to protected members in Scala is also a bit more restrictive than
in Java. In Scala, a protected member is only accessible from subclasses
of the class in which the member is defined. In Java such accesses are also
possible from other classes in the same package. In Scala, there is another
way to achieve this effect3 so protected is free to be left as is. The example
shown in Listing 13.11 illustrates protected accesses.
package p {
class Super {
protected def f() = { println("f") }
}
class Sub extends Super {
f()
}
class Other {
(new Super).f()
// error: f is not accessible
}
}
Listing 13.11 · How protected access differs in Scala and Java.
In Listing 13.11, the access to f in class Sub is OK because f is declared
protected in Super and Sub is a subclass of Super. By contrast the access
to f in Other is not permitted, because Other does not inherit from Super.
In Java, the latter access would be still permitted because Other is in the
same package as Sub.
Prepared for Library Genesis
Public members
Scala has no explicit modifier for public members: Any member not labeled
private or protected is public. Public members can be accessed from
anywhere.
3 Using
qualifiers, described in “Scope of protection” on page 288.
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package bobsrockets
package navigation {
private[bobsrockets] class Navigator {
protected[navigation] def useStarChart() = {}
class LegOfJourney {
private[Navigator] val distance = 100
}
private[this] var speed = 200
}
}
package launch {
import navigation._
object Vehicle {
private[launch] val guide = new Navigator
}
}
Listing 13.12 · Flexible scope of protection with access qualifiers.
Prepared for Library Genesis
Scope of protection
Access modifiers in Scala can be augmented with qualifiers. A modifier
of the form private[X] or protected[X] means that access is private or
protected “up to” X, where X designates some enclosing package, class or
singleton object.
Qualified access modifiers give you very fine-grained control over visibility. In particular they enable you to express Java’s accessibility notions,
such as package private, package protected, or private up to outermost class,
which are not directly expressible with simple modifiers in Scala. But they
also let you express accessibility rules that cannot be expressed in Java.
Listing 13.12 presents an example with many access qualifiers being
used. In this listing, class Navigator is labeled private[bobsrockets].
This means that this class is visible in all classes and objects that are contained in package bobsrockets. In particular, the access to Navigator
in object Vehicle is permitted because Vehicle is contained in package
launch, which is contained in bobsrockets. On the other hand, all code
outside the package bobsrockets cannot access class Navigator.
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This technique is quite useful in large projects that span several packages.
It allows you to define things that are visible in several sub-packages of your
project but that remain hidden from clients external to your project. The
same technique is not possible in Java. There, once a definition escapes its
immediate package boundary, it is visible to the world at large.
Of course, the qualifier of a private may also be the directly enclosing
package. An example is the access modifier of guide in object Vehicle
in Listing 13.12. Such an access modifier is equivalent to Java’s packageprivate access.
Table 13.1 · Effects of private qualifiers on LegOfJourney.distance
no access modifier
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private[bobsrockets]
private[navigation]
private[Navigator]
private[LegOfJourney]
private[this]
public access
access within outer package
same as package visibility in Java
same as private in Java
same as private in Scala
access only from same object
All qualifiers can also be applied to protected, with the same meaning
as private. That is, a modifier protected[X] in a class C allows access
to the labeled definition in all subclasses of C and also within the enclosing
package, class, or object X. For instance, the useStarChart method in Listing 13.12 is accessible in all subclasses of Navigator and also in all code
contained in the enclosing package navigation. It thus corresponds exactly
to the meaning of protected in Java.
The qualifiers of private can also refer to an enclosing class or object.
For instance the distance variable in class LegOfJourney in Listing 13.12
is labeled private[Navigator], so it is visible from everywhere in class
Navigator. This gives the same access capabilities as for private members
of inner classes in Java. A private[C] where C is the outermost enclosing
class is the same as just private in Java.
Finally, Scala also has an access modifier that is even more restrictive
than private. A definition labeled private[this] is accessible only from
within the same object that contains the definition. Such a definition is called
object-private. For instance, the definition of speed in class Navigator in
Listing 13.12 is object-private. This means that any access must not only be
within class Navigator, it must also be made from the very same instance of
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Navigator. Thus the accesses “speed” and “this.speed” would be legal
from within Navigator.
The following access, though, would not be allowed, even if it appeared
inside class Navigator:
val other = new Navigator
other.speed // this line would not compile
Marking a member private[this] is a guarantee that it will not be seen
from other objects of the same class. This can be useful for documentation. It also sometimes lets you write more general variance annotations (see
Section 19.7 for details).
To summarize, Table 13.1 on page 289 lists the effects of private qualifiers. Each line shows a qualified private modifier and what it would mean
if such a modifier were attached to the distance variable declared in class
LegOfJourney in Listing 13.12.
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Visibility and companion objects
In Java, static members and instance members belong to the same class, so
access modifiers apply uniformly to them. You have already seen that in
Scala there are no static members; instead you can have a companion object
that contains members that exist only once. For instance, in Listing 13.13
object Rocket is a companion of class Rocket.
Scala’s access rules privilege companion objects and classes when it
comes to private or protected accesses. A class shares all its access rights
with its companion object and vice versa. In particular, an object can access all private members of its companion class, just as a class can access all
private members of its companion object.
For instance, the Rocket class in Listing 13.13 can access method fuel,
which is declared private in object Rocket. Analogously, the Rocket object can access the private method canGoHomeAgain in class Rocket.
One exception where the similarity between Scala and Java breaks down
concerns protected static members. A protected static member of a Java
class C can be accessed in all subclasses of C. By contrast, a protected
member in a companion object makes no sense, as singleton objects don’t
have any subclasses.
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class Rocket {
import Rocket.fuel
private def canGoHomeAgain = fuel > 20
}
object Rocket {
private def fuel = 10
def chooseStrategy(rocket: Rocket) = {
if (rocket.canGoHomeAgain)
goHome()
else
pickAStar()
}
def goHome() = {}
def pickAStar() = {}
}
Listing 13.13: Accessing private members of companion classes and objects.
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13.6
Package objects
So far, the only code you have seen added to packages are classes, traits,
and standalone objects. These are by far the most common definitions that
are placed at the top level of a package. But Scala doesn’t limit you to just
those—Any kind of definition that you can put inside a class can also be at
the top level of a package. If you have some helper method you’d like to be
in scope for an entire package, go ahead and put it right at the top level of
the package.
To do so, put the definitions in a package object. Each package is allowed
to have one package object. Any definitions placed in a package object are
considered members of the package itself.
An example is shown in Listing 13.14. File package.scala holds a
package object for package bobsdelights. Syntactically, a package object looks much like one of the curly-braces packagings shown earlier in the
chapter. The only difference is that it includes the object keyword. It’s
a package object, not a package. The contents of the curly braces can include any definitions you like. In this case, the package object includes the
showFruit utility method from Listing 13.8.
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Given that definition, any other code in any package can import the
method just like it would import a class. For example, Listing 13.14 also
shows the standalone object PrintMenu, which is located in a different package. PrintMenu can import the utility method showFruit in the same way
it would import the class Fruit.
// In file bobsdelights/package.scala
package object bobsdelights {
def showFruit(fruit: Fruit) = {
import fruit._
println(name + "s are " + color)
}
}
// In file PrintMenu.scala
package printmenu
import bobsdelights.Fruits
import bobsdelights.showFruit
object PrintMenu {
def main(args: Array[String]) = {
for (fruit <- Fruits.menu) {
showFruit(fruit)
}
}
}
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Listing 13.14 · A package object.
Looking ahead, there are other uses of package objects for kinds of
definitions you haven’t seen yet. Package objects are frequently used to
hold package-wide type aliases (Chapter 20) and implicit conversions (Chapter 21). The top-level scala package has a package object, and its definitions
are available to all Scala code.
Package objects are compiled to class files named package.class that
are the located in the directory of the package that they augment. It’s useful
to keep the same convention for source files. So you would typically put the
source file of the package object bobsdelights of Listing 13.14 into a file
named package.scala that resides in the bobsdelights directory.
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Section 13.7
13.7
Chapter 13 · Packages and Imports
Conclusion
Prepared for Library Genesis
In this chapter, you saw the basic constructs for dividing a program into
packages. This gives you a simple and useful kind of modularity, so that
you can work with very large bodies of code without different parts of the
code trampling on each other. Scala’s system is the same in spirit as Java’s
packages, but there are some differences where Scala chooses to be more
consistent or more general.
Looking ahead, Chapter 29 describes a more flexible module system than
division into packages. In addition to letting you separate code into several
namespaces, that approach allows modules to be parameterized and inherit
from each other. In the next chapter, we’ll turn our attention to assertions
and unit testing.
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Chapter 14
Assertions and Tests
Assertions and tests are two important ways you can check that the software
you write behaves as you expect. In this chapter, we’ll show you several
options you have in Scala to write and run them.
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14.1
Assertions
Assertions in Scala are written as calls of a predefined method assert.1 The
expression assert(condition) throws an AssertionError if condition
does not hold. There’s also a two-argument version of assert: The expression assert(condition, explanation) tests condition and, if it does
not hold, throws an AssertionError that contains the given explanation.
The type of explanation is Any, so you can pass any object as the explanation. The assert method will call toString on it to get a string explanation
to place inside the AssertionError. For example, in the method named
“above” of class Element, shown in Listing 10.13 on page 243, you might
place an assert after the calls to widen to make sure that the widened elements have equal widths. This is shown in Listing 14.1.
Another way you might choose to do this is to check the widths at the
end of the widen method, right before you return the value. You can accomplish this by storing the result in a val, performing an assertion on the result,
then mentioning the val last so the result is returned if the assertion succeeds. However, you can do this more concisely with a convenience method
in Predef named ensuring, as shown in Listing 14.2.
1 The assert method is defined in the Predef singleton object, whose members are
automatically imported into every Scala source file.
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Section 14.1
Chapter 14 · Assertions and Tests
def above(that: Element): Element = {
val this1 = this widen that.width
val that1 = that widen this.width
assert(this1.width == that1.width)
elem(this1.contents ++ that1.contents)
}
Listing 14.1 · Using an assertion.
The ensuring method can be used with any result type because of an
implicit conversion. Although it looks in this code as if we’re invoking
ensuring on widen’s result, which is type Element, we’re actually invoking ensuring on a type to which Element is implicitly converted. The
ensuring method takes one argument, a predicate function that takes a result type and returns Boolean, and passes the result to the predicate. If the
predicate returns true, ensuring will return the result; otherwise, ensuring
will throw an AssertionError.
In this example, the predicate is “w <= _.width”. The underscore is a
placeholder for the one argument passed to the predicate, the Element result
of the widen method. If the width passed as w to widen is less than or equal
to the width of the result Element, the predicate will result in true, and
ensuring will result in the Element on which it was invoked. Because this
is the last expression of the widen method, widen itself will then result in
the Element.
Assertions can be enabled and disabled using the JVM’s -ea and -da
private def widen(w: Int): Element =
if (w <= width)
this
else {
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val left = elem(' ', (w - width) / 2, height)
var right = elem(' ', w - width - left.width, height)
left beside this beside right
} ensuring (w <= _.width)
Listing 14.2 · Using ensuring to assert a function’s result.
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command-line flags. When enabled, each assertion serves as a little test that
uses the actual data encountered as the software runs. In the remainder of
this chapter, we’ll focus on the writing of external tests, which provide their
own test data and run independently from the application.
14.2
Testing in Scala
You have many options for testing in Scala, from established Java tools, such
as JUnit and TestNG, to tools written in Scala, such as ScalaTest, specs2, and
ScalaCheck. For the remainder of this chapter, we’ll give you a quick tour
of these tools. We’ll start with ScalaTest.
ScalaTest is the most flexible Scala test framework: it can be easily customized to solve different problems. ScalaTest’s flexibility means teams can
use whatever testing style fits their needs best. For example, for teams familar with JUnit, the FunSuite style will feel comfortable and familiar. Listing 14.3 shows an example.
import org.scalatest.FunSuite
import Element.elem
class ElementSuite extends FunSuite {
test("elem result should have passed width") {
val ele = elem('x', 2, 3)
assert(ele.width == 2)
}
}
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Listing 14.3 · Writing tests with FunSuite.
The central concept in ScalaTest is the suite, a collection of tests. A
test can be anything with a name that can start and either succeed, fail, be
pending, or canceled. Trait Suite is the central unit of composition in ScalaTest. Suite declares “lifecycle” methods defining a default way to run tests,
which can be overridden to customize how tests are written and run.
ScalaTest offers style traits that extend Suite and override lifecycle
methods to support different testing styles. It also provides mixin traits that
override lifecycle methods to address particular testing needs. You define
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test classes by composing Suite style and mixin traits, and define test suites
by composing Suite instances.
FunSuite, which is extended by the test class shown in Listing 14.3, is
an example of a testing style. The “Fun” in FunSuite stands for function;
“test” is a method defined in FunSuite, which is invoked by the primary
constructor of ElementSuite. You specify the name of the test as a string
between the parentheses and the test code itself between curly braces. The
test code is a function passed as a by-name parameter to test, which registers it for later execution.
ScalaTest is integrated into common build tools (such as sbt and Maven)
and IDEs (such as IntelliJ IDEA and Eclipse). You can also run a Suite directly via ScalaTest’s Runner application or from the Scala interpreter simply by invoking execute on it. Here’s an example:
scala> (new ElementSuite).execute()
ElementSuite:
- elem result should have passed width
All ScalaTest styles, including FunSuite, are designed to encourage the
writing of focused tests with descriptive names. In addition, all styles generate specification-like output that can facilitate communication among stakeholders. The style you choose dictates only how the declarations of your tests
will look. Everything else in ScalaTest works consistently the same way no
matter what style you choose.2
14.3
Informative failure reports
The test in Listing 14.3 attempts to create an element of width 2 and assert
that the width of the resulting element is indeed 2. Were this assertion to
fail, the failure report would include the filename and line number of the
offending assertion, and an informative error message:
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scala> val width = 3
width: Int = 3
scala> assert(width == 2)
org.scalatest.exceptions.TestFailedException:
3 did not equal 2
2 More
detail on ScalaTest is available from http://www.scalatest.org/.
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To provide descriptive error messages when assertions fail, ScalaTest analyzes the expressions passed to each assert invocation at compile time. If
you prefer to see even more detailed information about assertion failures, you
can use ScalaTest’s DiagrammedAssertions, whose error messages display
a diagram of the expression passed to assert:
scala> assert(List(1, 2, 3).contains(4))
org.scalatest.exceptions.TestFailedException:
assert(List(1, 2, 3).contains(4))
|
|
|
|
|
|
|
1
2
3
false
4
List(1, 2, 3)
ScalaTest’s assert methods do not differentiate between the actual and
expected result in error messages. They just indicate that the left operand did
not equal the right operand, or show the values in a diagram. If you wish to
emphasize the distinction between actual and expected, you can alternatively
use ScalaTest’s assertResult method, like this:
assertResult(2) {
ele.width
}
With this expression you indicate that you expect the code between the curly
braces to result in 2. Were the code between the braces to result in 3, you’d
see the message, “Expected 2, but got 3” in the test failure report.
If you want to check that a method throws an expected exception, you
can use ScalaTest’s assertThrows method, like this:
assertThrows[IllegalArgumentException] {
elem('x', -2, 3)
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}
If the code between the curly braces throws a different exception than expected, or throws no exception, assertThrows will complete abruptly with
a TestFailedException. You’ll get a helpful error message in the failure
report, such as:
Expected IllegalArgumentException to be thrown,
but NegativeArraySizeException was thrown.
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On the other hand, if the code completes abruptly with an instance of the
passed exception class, assertThrows will return normally. If you wish to
inspect the expected exception further, you can use intercept instead of
assertThrows. The intercept method works the same as assertThrows,
except if the expected exception is thrown, intercept returns it:
val caught =
intercept[ArithmeticException] {
1 / 0
}
assert(caught.getMessage == "/ by zero")
In short, ScalaTest’s assertions work hard to provide useful failure messages that will help you diagnose and fix problems in your code.
14.4
Tests as specifications
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In the behavior-driven development (BDD) testing style, the emphasis is on
writing human-readable specifications of the expected behavior of code and
accompanying tests that verify the code has the specified behavior. ScalaTest
includes several traits that facilitate this style of testing. An example using
one such trait, FlatSpec, is shown in Listing 14.4.
In a FlatSpec, you write tests as specifier clauses. You start by writing
a name for the subject under test as a string ("A UniformElement" in Listing 14.4), then should (or must or can), then a string that specifies a bit of
behavior required of the subject, then in. In the curly braces following in,
you write code that tests the specified behavior. In subsequent clauses you
can write it to refer to the most recently given subject. When a FlatSpec
is executed, it will run each specifier clause as a ScalaTest test. FlatSpec
(and ScalaTest’s other specification traits) generate output that reads like a
specification when run. For example, here’s what the output will look like if
you run ElementSpec from Listing 14.4 in the interpreter:
scala> (new ElementSpec).execute()
A UniformElement
- should have a width equal to the passed value
- should have a height equal to the passed value
- should throw an IAE if passed a negative width
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import org.scalatest.FlatSpec
import org.scalatest.Matchers
import Element.elem
class ElementSpec extends FlatSpec with Matchers {
"A UniformElement" should
"have a width equal to the passed value" in {
val ele = elem('x', 2, 3)
ele.width should be (2)
}
it should "have a height equal to the passed value" in {
val ele = elem('x', 2, 3)
ele.height should be (3)
}
it should "throw an IAE if passed a negative width" in {
an [IllegalArgumentException] should be thrownBy {
elem('x', -2, 3)
}
}
}
Listing 14.4 · Specifying and testing behavior with a ScalaTest FlatSpec.
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Listing 14.4 also illustrates ScalaTest’s matchers domain-specific language (DSL). By mixing in trait Matchers, you can write assertions that
read more like natural language. ScalaTest provides many matchers in its
DSL, and also enables you to define new matchers with custom failure messages. The matchers shown in Listing 14.4 include the “should be” and
“an [ . . . ] should be thrownBy { . . . } ” syntax. You can alternatively mix
in MustMatchers if you prefer must to should. For example, mixing in
MustMatchers would allow you to write expressions such as:
result must be >= 0
map must contain key 'c'
If the last assertion failed, you’d see an error message similar to:
Map('a' -> 1, 'b' -> 2) did not contain key 'c'
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The specs2 testing framework, an open source tool written in Scala by
Eric Torreborre, also supports the BDD style of testing but with a different
syntax. For example, you could use specs2 to write the test shown in Listing 14.5:
import org.specs2._
import Element.elem
object ElementSpecification extends Specification {
"A UniformElement" should {
"have a width equal to the passed value" in {
val ele = elem('x', 2, 3)
ele.width must be_==(2)
}
"have a height equal to the passed value" in {
val ele = elem('x', 2, 3)
ele.height must be_==(3)
}
"throw an IAE if passed a negative width" in {
elem('x', -2, 3) must
throwA[IllegalArgumentException]
}
}
}
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Listing 14.5 · Specifying and testing behavior with the specs2 framework.
Like ScalaTest, specs2 provides a matchers DSL. You can see some examples of specs2 matchers in action in Listing 14.5 in the lines that contain
“must be_==” and “must throwA”.3 You can use specs2 standalone, but it
is also integrated with ScalaTest and JUnit, so you can run specs2 tests with
those tools as well.
One of the big ideas of BDD is that tests can be used to facilitate communication between the people who decide what a software system should
do, the people who implement the software, and the people who determine
whether the software is finished and working. Although any of ScalaTest’s
3 You
can download specs2 from http://specs2.org/.
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or specs2’s styles can be used in this manner, ScalaTest’s FeatureSpec in
particular is designed for it. Listing 14.6 shows an example:
import org.scalatest._
class TVSetSpec extends FeatureSpec with GivenWhenThen {
feature("TV power button") {
scenario("User presses power button when TV is off") {
Given("a TV set that is switched off")
When("the power button is pressed")
Then("the TV should switch on")
pending
}
}
}
Listing 14.6 · Using tests to facilitate communication among stakeholders.
FeatureSpec is designed to guide conversations about software require-
ments: You must identify specific features, then specify those features in
terms of scenarios. The Given, When, and Then methods (provided by trait
GivenWhenThen) can help focus the conversation on the specifics of individual scenarios. The pending call at the end indicates that neither the test nor
the actual behavior has been implemented—just the specification. Once all
the tests and specified behavior have been implemented, the tests will pass
and the requirements can be deemed to have been met.
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14.5
Property-based testing
Another useful testing tool for Scala is ScalaCheck, an open source framework written by Rickard Nilsson. ScalaCheck enables you to specify properties that the code under test must obey. For each property, ScalaCheck
will generate data and execute assertions that check whether the property
holds. Listing 14.7 shows an example of using ScalaCheck from a ScalaTest
WordSpec that mixes in trait PropertyChecks.
WordSpec is a ScalaTest style class. The PropertyChecks trait provides several forAll methods that allow you to mix property-based tests
with traditional assertion-based or matcher-based tests. In this example, we
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import org.scalatest.WordSpec
import org.scalatest.prop.PropertyChecks
import org.scalatest.MustMatchers._
import Element.elem
class ElementSpec extends WordSpec with PropertyChecks {
"elem result" must {
"have passed width" in {
forAll { (w: Int) =>
whenever (w > 0) {
elem('x', w, 3).width must equal (w)
}
}
}
}
}
Listing 14.7 · Writing property-based tests with ScalaCheck.
check a property that the elem factory should obey. ScalaCheck properties
are expressed as function values that take as parameters the data needed by
the property’s assertions. This data will be generated by ScalaCheck. In the
property shown in Listing 14.7, the data is an integer named w that represents
a width. Inside the body of the function, you see this code:
whenever (w > 0) {
elem('x', w, 3).width must equal (w)
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}
The whenever clause indicates that whenever the left hand expression is true,
the expression on the right must hold true. Thus in this case, the expression
in the block must hold true whenever w is greater than 0. The right-hand
expression in this case will yield true if the width passed to the elem factory
is the same as the width of the Element returned by the factory.
With this small amount of code, ScalaCheck will generate possibly hundreds of values for w and test each one, looking for a value for which the
property doesn’t hold. If the property holds true for every value ScalaCheck
tries, the test will pass. Otherwise, the test will complete abruptly with a
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TestFailedException that contains information including the value that
caused the failure.
14.6
Organizing and running tests
Each framework mentioned in this chapter provides some mechanism for
organizing and running tests. In this section, we’ll give a quick overview
of ScalaTest’s approach. To get the full story on any of these frameworks,
however, you’ll need to consult their documentation.
In ScalaTest, you organize large test suites by nesting Suites inside
Suites. When a Suite is executed, it will execute its nested Suites as
well as its tests. The nested Suites will in turn execute their nested Suites,
and so on. A large test suite, therefore, is represented as a tree of Suite
objects. When you execute the root Suite in the tree, all Suites in the tree
will be executed.
You can nest suites manually or automatically. To nest manually, you either override the nestedSuites method on your Suites or pass the Suites
you want to nest to the constructor of class Suites, which ScalaTest provides for this purpose. To nest automatically, you provide package names
to ScalaTest’s Runner, which will discover Suites automatically, nest them
under a root Suite, and execute the root Suite.
You can invoke ScalaTest’s Runner application from the command line
or via a build tool, such as sbt, maven, or ant. The simplest way to invoke
Runner on the command line is via the org.scalatest.run application.
This application expects a fully qualified test class name. For example, to
run the test class shown in Listing 14.6, you must compile it with:
$ scalac -cp scalatest.jar TVSetSpec.scala
Then you can run it with:
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$ scala -cp scalatest.jar org.scalatest.run TVSetSpec
With -cp you place ScalaTest’s JAR file on the class path. (When downloaded, the JAR file name will include embedded Scala and ScalaTest version
numbers.) The next token, org.scalatest.run, is the fully qualified application name. Scala will run this application and pass the remaining tokens
as command line arguments. The TVSetSpec argument specifies the suite to
execute. The result is shown in Figure 14.1.
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Figure 14.1 · The output of org.scalatest.run.
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14.7
Conclusion
In this chapter you saw examples of mixing assertions directly in production code, as well as writing them externally in tests. You saw that as a
Scala programmer, you can take advantage of popular testing tools from the
Java community, such as JUnit and TestNG, as well as newer tools designed
explicitly for Scala, such as ScalaTest, ScalaCheck, and specs2. Both incode assertions and external tests can help you achieve your software quality
goals. We felt that these techniques are important enough to justify the short
detour from the Scala tutorial that this chapter represented. In the next chapter, however, we’ll return to the language tutorial and cover a very useful
aspect of Scala: pattern matching.
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305
Chapter 15
Case Classes and Pattern Matching
This chapter introduces case classes and pattern matching, twin constructs
that support you when writing regular, non-encapsulated data structures.
These two constructs are particularly helpful for tree-like recursive data.
If you have programmed in a functional language before, then you will
probably recognize pattern matching. But case classes will be new to you.
Case classes are Scala’s way to allow pattern matching on objects without
requiring a large amount of boilerplate. Generally, all you need to do is add
a single case keyword to each class that you want to be pattern matchable.
This chapter starts with a simple example of case classes and pattern
matching. It then goes through all of the kinds of patterns that are supported,
talks about the role of sealed classes, discusses the Option type, and shows
some non-obvious places in the language where pattern matching is used.
Finally, a larger, more realistic example of pattern matching is shown.
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15.1
A simple example
Before delving into all the rules and nuances of pattern matching, it is worth
looking at a simple example to get the general idea. Let’s say you need to
write a library that manipulates arithmetic expressions, perhaps as part of a
domain-specific language you are designing.
A first step to tackling this problem is the definition of the input data. To
keep things simple, we’ll concentrate on arithmetic expressions consisting
of variables, numbers, and unary and binary operations. This is expressed by
the hierarchy of Scala classes shown in Listing 15.1.
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Chapter 15 · Case Classes and Pattern Matching
abstract class Expr
case class Var(name: String) extends Expr
case class Number(num: Double) extends Expr
case class UnOp(operator: String, arg: Expr) extends Expr
case class BinOp(operator: String,
left: Expr, right: Expr) extends Expr
Listing 15.1 · Defining case classes.
The hierarchy includes an abstract base class Expr with four subclasses,
one for each kind of expression being considered.1 The bodies of all five
classes are empty. As mentioned previously, in Scala you can leave out the
braces around an empty class body if you wish, so class C is the same as
class C {}.
Case classes
The other noteworthy thing about the declarations of Listing 15.1 is that each
subclass has a case modifier. Classes with such a modifier are called case
classes. Using the modifier makes the Scala compiler add some syntactic
conveniences to your class.
First, it adds a factory method with the name of the class. This means
that, for instance, you can write Var("x") to construct a Var object, instead
of the slightly longer new Var("x"):
scala> val v = Var("x")
v: Var = Var(x)
The factory methods are particularly nice when you nest them. Because there
are no noisy new keywords sprinkled throughout the code, you can take in
the expression’s structure at a glance:
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scala> val op = BinOp("+", Number(1), v)
op: BinOp = BinOp(+,Number(1.0),Var(x))
The second syntactic convenience is that all arguments in the parameter list
of a case class implicitly get a val prefix, so they are maintained as fields:
1 Instead
of an abstract class, we could have also chosen to model the root of that class
hierarchy as a trait. Modeling it as an abstract class may be slightly more efficient.
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scala> v.name
res0: String = x
scala> op.left
res1: Expr = Number(1.0)
Third, the compiler adds “natural” implementations of methods toString,
hashCode, and equals to your class. They will print, hash, and compare a
whole tree consisting of the class and (recursively) all its arguments. Since
== in Scala always delegates to equals, this means that elements of case
classes are always compared structurally:
scala> println(op)
BinOp(+,Number(1.0),Var(x))
scala> op.right == Var("x")
res3: Boolean = true
Finally, the compiler adds a copy method to your class for making modified
copies. This method is useful for making a new instance of the class that is
the same as another one except that one or two attributes are different. The
method works by using named and default parameters (see Section 8.8). You
specify the changes you’d like to make by using named parameters. For any
parameter you don’t specify, the value from the old object is used. As an
example, here is how you can make an operation just like op except that the
operator has changed:
scala> op.copy(operator = "-")
res4: BinOp = BinOp(-,Number(1.0),Var(x))
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All these conventions add a lot of convenience—at a small price. You
have to write the case modifier, and your classes and objects become a bit
larger. They are larger because additional methods are generated and an
implicit field is added for each constructor parameter. However, the biggest
advantage of case classes is that they support pattern matching.
Pattern matching
Say you want to simplify arithmetic expressions of the kinds just presented.
There is a multitude of possible simplification rules. The following three
rules just serve as an illustration:
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UnOp("-", UnOp("-", e))
=> e
// Double negation
BinOp("+", e, Number(0)) => e
// Adding zero
BinOp("*", e, Number(1)) => e
// Multiplying by one
Using pattern matching, these rules can be taken almost as they are to form
the core of a simplification function in Scala, as shown in Listing 15.2. The
function, simplifyTop, can be used like this:
scala> simplifyTop(UnOp("-", UnOp("-", Var("x"))))
res4: Expr = Var(x)
def simplifyTop(expr: Expr): Expr = expr match {
case UnOp("-", UnOp("-", e))
=> e
// Double negation
case BinOp("+", e, Number(0)) => e
// Adding zero
case BinOp("*", e, Number(1)) => e
case _ => expr
// Multiplying by one
}
Listing 15.2 · The simplifyTop function, which does a pattern match.
The right-hand side of simplifyTop consists of a match expression.
match corresponds to switch in Java, but it’s written after the selector expression. In other words, it’s:
selector match { alternatives }
instead of:
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switch (selector) { alternatives }
A pattern match includes a sequence of alternatives, each starting with the
keyword case. Each alternative includes a pattern and one or more expressions, which will be evaluated if the pattern matches. An arrow symbol =>
separates the pattern from the expressions.
A match expression is evaluated by trying each of the patterns in the
order they are written. The first pattern that matches is selected, and the part
following the arrow is selected and executed.
A constant pattern like "+" or 1 matches values that are equal to the
constant with respect to ==. A variable pattern like e matches every value.
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The variable then refers to that value in the right hand side of the case clause.
In this example, note that the first three alternatives evaluate to e, a variable
that is bound within the associated pattern. The wildcard pattern (_) also
matches every value, but it does not introduce a variable name to refer to that
value. In Listing 15.2, notice how the match ends with a default case that
does nothing to the expression. Instead, it just results in expr, the expression
matched upon.
A constructor pattern looks like UnOp("-", e). This pattern matches
all values of type UnOp whose first argument matches "-" and whose second argument matches e. Note that the arguments to the constructor are
themselves patterns. This allows you to write deep patterns using a concise
notation. Here’s an example:
UnOp("-", UnOp("-", e))
Imagine trying to implement this same functionality using the visitor design
pattern!2 Almost as awkward, imagine implementing it as a long sequence
of if statements, type tests, and type casts.
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match compared to switch
Match expressions can be seen as a generalization of Java-style switches. A
Java-style switch can be naturally expressed as a match expression, where
each pattern is a constant and the last pattern may be a wildcard (which
represents the default case of the switch).
However, there are three differences to keep in mind: First, match is an
expression in Scala (i.e., it always results in a value). Second, Scala’s alternative expressions never “fall through” into the next case. Third, if none of
the patterns match, an exception named MatchError is thrown. This means
you always have to make sure that all cases are covered, even if it means
adding a default case where there’s nothing to do.
Listing 15.3 shows an example. The second case is necessary because
without it, the match expression would throw a MatchError for every expr
argument that is not a BinOp. In this example, no code is specified for that
second case, so if that case runs it does nothing. The result of either case is
the unit value ‘()’, which is also the result of the entire match expression.
2 Gamma,
et al., Design Patterns [Gam95]
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expr match {
case BinOp(op, left, right) =>
println(expr + " is a binary operation")
case _ =>
}
Listing 15.3 · A pattern match with an empty “default” case.
15.2
Kinds of patterns
The previous example showed several kinds of patterns in quick succession.
Now take a minute to look at each pattern in detail.
The syntax of patterns is easy, so do not worry about that too much.
All patterns look exactly like the corresponding expression. For instance,
given the hierarchy of Listing 15.1, the pattern Var(x) matches any variable
expression, binding x to the name of the variable. Used as an expression,
Var(x)—exactly the same syntax—recreates an equivalent object, assuming
x is already bound to the variable’s name. Since the syntax of patterns is so
transparent, the main thing to pay attention to is just what kinds of patterns
are possible.
Wildcard patterns
The wildcard pattern (_) matches any object whatsoever. You have already
seen it used as a default, catch-all alternative, like this:
expr match {
case BinOp(op, left, right) =>
println(expr + " is a binary operation")
case _ => // handle the default case
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}
Wildcards can also be used to ignore parts of an object that you do not care
about. For example, the previous example does not actually care what the
elements of a binary operation are; it just checks whether or not it is a binary
operation. Thus, the code can just as well use the wildcard pattern for the
elements of the BinOp, as shown in Listing 15.4.
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expr match {
case BinOp(_, _, _) => println(expr + " is a binary operation")
case _ => println("It's something else")
}
Listing 15.4 · A pattern match with wildcard patterns.
Constant patterns
A constant pattern matches only itself. Any literal may be used as a constant.
For example, 5, true, and "hello" are all constant patterns. Also, any val
or singleton object can be used as a constant. For example, Nil, a singleton
object, is a pattern that matches only the empty list. Listing 15.5 shows some
examples of constant patterns:
def describe(x: Any) = x match {
case 5 => "five"
case true => "truth"
case "hello" => "hi!"
case Nil => "the empty list"
case _ => "something else"
}
Listing 15.5 · A pattern match with constant patterns.
Here is how the pattern match shown in Listing 15.5 looks in action:
scala> describe(5)
res6: String = five
scala> describe(true)
res7: String = truth
scala> describe("hello")
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res8: String = hi!
scala> describe(Nil)
res9: String = the empty list
scala> describe(List(1,2,3))
res10: String = something else
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Variable patterns
A variable pattern matches any object, just like a wildcard. But unlike a
wildcard, Scala binds the variable to whatever the object is. You can then use
this variable to act on the object further. For example, Listing 15.6 shows a
pattern match that has a special case for zero, and a default case for all other
values. The default case uses a variable pattern so that it has a name for the
value, no matter what it is.
expr match {
case 0 => "zero"
case somethingElse => "not zero: " + somethingElse
}
Listing 15.6 · A pattern match with a variable pattern.
Variable or constant?
Constant patterns can have symbolic names. You saw this already when we
used Nil as a pattern. Here is a related example, where a pattern match
involves the constants E (2.71828. . . ) and Pi (3.14159. . . ):
scala> import math.{E, Pi}
import math.{E, Pi}
scala> E match {
case Pi => "strange math? Pi = " + Pi
case _ => "OK"
}
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res11: String = OK
As expected, E does not match Pi, so the “strange math” case is not used.
How does the Scala compiler know that Pi is a constant imported from
scala.math, and not a variable that stands for the selector value itself? Scala
uses a simple lexical rule for disambiguation: a simple name starting with
a lowercase letter is taken to be a pattern variable; all other references are
taken to be constants. To see the difference, create a lowercase alias for pi
and try with that:
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scala> val pi = math.Pi
pi: Double = 3.141592653589793
scala> E match {
case pi => "strange math? Pi = " + pi
}
res12: String = strange math? Pi = 2.718281828459045
Here the compiler will not even let you add a default case at all. Since
pi is a variable pattern, it will match all inputs, and so no cases following it
can be reached:
scala> E match {
case pi => "strange math? Pi = " + pi
case _ => "OK"
}
:12: warning: unreachable code
case _ => "OK"
ˆ
You can still use a lowercase name for a pattern constant, if you need
to, by using one of two tricks. First, if the constant is a field of some object, you can prefix it with a qualifier. For instance, pi is a variable pattern,
but this.pi or obj.pi are constants even though they start with lowercase
letters. If that does not work (because pi is a local variable, say), you can alternatively enclose the variable name in back ticks. For instance, `pi` would
again be interpreted as a constant, not as a variable:
scala> E match {
case `pi` => "strange math? Pi = " + pi
case _ => "OK"
}
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res14: String = OK
As you can see, the back-tick syntax for identifiers is used for two different
purposes in Scala to help you code your way out of unusual circumstances.
Here you see that it can be used to treat a lowercase identifier as a constant in
a pattern match. Earlier on, in Section 6.10, you saw that it can also be used
to treat a keyword as an ordinary identifier, e.g., writing Thread.`yield`()
treats yield as an identifier rather than a keyword.
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Constructor patterns
Constructors are where pattern matching becomes really powerful. A constructor pattern looks like “BinOp("+", e, Number(0))”. It consists of a
name (BinOp) and then a number of patterns within parentheses: "+", e,
and Number(0). Assuming the name designates a case class, such a pattern
means to first check that the object is a member of the named case class, and
then to check that the constructor parameters of the object match the extra
patterns supplied.
These extra patterns mean that Scala patterns support deep matches.
Such patterns not only check the top-level object supplied, but also the contents of the object against further patterns. Since the extra patterns can themselves be constructor patterns, you can use them to check arbitrarily deep
into an object. For example, the pattern shown in Listing 15.7 checks that the
top-level object is a BinOp, that its third constructor parameter is a Number,
and that the value field of that number is 0. This pattern is one line long yet
checks three levels deep.
expr match {
case BinOp("+", e, Number(0)) => println("a deep match")
case _ =>
}
Listing 15.7 · A pattern match with a constructor pattern.
Sequence patterns
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You can match against sequence types, like List or Array, just like you
match against case classes. Use the same syntax, but now you can specify
any number of elements within the pattern. Listing 15.8 shows a pattern that
checks for a three-element list starting with zero.
expr match {
case List(0, _, _) => println("found it")
case _ =>
}
Listing 15.8 · A sequence pattern with a fixed length.
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If you want to match against a sequence without specifying how long it
can be, you can specify _* as the last element of the pattern. This funnylooking pattern matches any number of elements within a sequence, including zero elements. Listing 15.9 shows an example that matches any list that
starts with zero, regardless of how long the list is.
expr match {
case List(0, _*) => println("found it")
case _ =>
}
Listing 15.9 · A sequence pattern with an arbitrary length.
Tuple patterns
You can match against tuples too. A pattern like (a, b, c) matches an
arbitrary 3-tuple. An example is shown in Listing 15.10.
def tupleDemo(expr: Any) =
expr match {
case (a, b, c)
=>
println("matched " + a + b + c)
case _ =>
}
Listing 15.10 · A pattern match with a tuple pattern.
If you load the tupleDemo method shown in Listing 15.10 into the interpreter, and pass to it a tuple with three elements, you’ll see:
scala> tupleDemo(("a ", 3, "-tuple"))
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matched a 3-tuple
Typed patterns
You can use a typed pattern as a convenient replacement for type tests and
type casts. Listing 15.11 shows an example.
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def generalSize(x: Any) = x match {
case s: String => s.length
case m: Map[_, _] => m.size
case _ => -1
}
Listing 15.11 · A pattern match with typed patterns.
Here are a few examples of using generalSize in the Scala interpreter:
scala> generalSize("abc")
res16: Int = 3
scala> generalSize(Map(1 -> 'a', 2 -> 'b'))
res17: Int = 2
scala> generalSize(math.Pi)
res18: Int = -1
The generalSize method returns the size or length of objects of various
types. Its argument is of type Any, so it could be any value. If the argument is
a String, the method returns the string’s length. The pattern “s: String” is
a typed pattern; it matches every (non-null) instance of String. The pattern
variable s then refers to that string.
Note that even though s and x refer to the same value, the type of x
is Any, while the type of s is String. So you can write s.length in the
alternative expression that corresponds to the pattern, but you could not write
x.length, because the type Any does not have a length member.
An equivalent but more long-winded way that achieves the effect of a
match against a typed pattern employs a type test followed by a type cast.
Scala uses a different syntax than Java for these. To test whether an expression expr has type String, say, you write:
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expr.isInstanceOf[String]
To cast the same expression to type String, you use:
expr.asInstanceOf[String]
Using a type test and cast, you could rewrite the first case of the previous
match expression as shown in Listing 15.12.
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if (x.isInstanceOf[String]) {
val s = x.asInstanceOf[String]
s.length
} else ...
Listing 15.12 · Using isInstanceOf and asInstanceOf (poor style).
The operators isInstanceOf and asInstanceOf are treated as predefined methods of class Any that take a type parameter in square brackets.
In fact, x.asInstanceOf[String] is a special case of a method invocation
with an explicit type parameter String.
As you will have noted by now, writing type tests and casts is rather
verbose in Scala. That’s intentional because it is not encouraged practice.
You are usually better off using a pattern match with a typed pattern. That’s
particularly true if you need to do both a type test and a type cast, because
both operations are then rolled into a single pattern match.
The second case of the match expression in Listing 15.11 contains the
typed pattern “m: Map[_, _]”. This pattern matches any value that is a Map of
some arbitrary key and value types, and lets m refer to that value. Therefore,
m.size is well typed and returns the size of the map. The underscores in the
type pattern3 are like wildcards in other patterns. You could have also used
(lowercase) type variables instead.
Type erasure
Can you also test for a map with specific element types? This would be
handy, say, for testing whether a given value is a map from type Int to type
Int. Let’s try:
scala> def isIntIntMap(x: Any) = x match {
case m: Map[Int, Int] => true
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case _ => false
}
:9: warning: non-variable type argument Int in type
pattern scala.collection.immutable.Map[Int,Int] (the
underlying of Map[Int,Int]) is unchecked since it is
3 In
the typed pattern, m: Map[_, _], the “Map[_, _]” portion is called a type pattern.
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eliminated by erasure
case m: Map[Int, Int] => true
ˆ
Scala uses the erasure model of generics, just like Java does. This means
that no information about type arguments is maintained at runtime. Consequently, there is no way to determine at runtime whether a given Map object
has been created with two Int arguments, rather than with arguments of different types. All the system can do is determine that a value is a Map of
some arbitrary type parameters. You can verify this behavior by applying
isIntIntMap to arguments of different instances of class Map:
scala> isIntIntMap(Map(1 -> 1))
res19: Boolean = true
scala> isIntIntMap(Map("abc" -> "abc"))
res20: Boolean = true
The first application returns true, which looks correct, but the second application also returns true, which might be a surprise. To alert you to the
possibly non-intuitive runtime behavior, the compiler emits unchecked warnings like the one shown previously.
The only exception to the erasure rule is arrays, because they are handled
specially in Java as well as in Scala. The element type of an array is stored
with the array value, so you can pattern match on it. Here’s an example:
scala> def isStringArray(x: Any) = x match {
case a: Array[String] => "yes"
case _ => "no"
}
isStringArray: (x: Any)String
scala> val as = Array("abc")
as: Array[String] = Array(abc)
scala> isStringArray(as)
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res21: String = yes
scala> val ai = Array(1, 2, 3)
ai: Array[Int] = Array(1, 2, 3)
scala> isStringArray(ai)
res22: String = no
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Variable binding
In addition to the standalone variable patterns, you can also add a variable
to any other pattern. You simply write the variable name, an at sign (@), and
then the pattern. This gives you a variable-binding pattern, which means the
pattern is to perform the pattern match as normal, and if the pattern succeeds,
set the variable to the matched object just as with a simple variable pattern.
As an example, Listing 15.13 shows a pattern match that looks for the
absolute value operation being applied twice in a row. Such an expression
can be simplified to only take the absolute value one time.
expr match {
case UnOp("abs", e @ UnOp("abs", _)) => e
case _ =>
}
Listing 15.13 · A pattern with a variable binding (via the @ sign).
The example shown in Listing 15.13 includes a variable-binding pattern
with e as the variable and UnOp("abs", _) as the pattern. If the entire pattern
match succeeds, then the portion that matched the UnOp("abs", _) part is
made available as variable e. The result of the case is just e, because e has
the same value as expr but with one less absolute value operation.
15.3
Pattern guards
Sometimes, syntactic pattern matching is not precise enough. For instance,
say you are given the task of formulating a simplification rule that replaces
sum expressions with two identical operands, such as e + e, by multiplications of two (e.g., e * 2). In the language of Expr trees, an expression like:
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BinOp("+", Var("x"), Var("x"))
would be transformed by this rule to:
BinOp("*", Var("x"), Number(2))
You might try to define this rule as follows:
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scala> def simplifyAdd(e: Expr) = e match {
case BinOp("+", x, x) => BinOp("*", x, Number(2))
case _ => e
}
:14: error: x is already defined as value x
case BinOp("+", x, x) => BinOp("*", x, Number(2))
ˆ
This fails because Scala restricts patterns to be linear: a pattern variable may
only appear once in a pattern. However, you can re-formulate the match with
a pattern guard, as shown in Listing 15.14:
scala> def simplifyAdd(e: Expr) = e match {
case BinOp("+", x, y) if x == y =>
BinOp("*", x, Number(2))
case _ => e
}
simplifyAdd: (e: Expr)Expr
Listing 15.14 · A match expression with a pattern guard.
A pattern guard comes after a pattern and starts with an if. The guard
can be an arbitrary boolean expression, which typically refers to variables
in the pattern. If a pattern guard is present, the match succeeds only if the
guard evaluates to true. Hence, the first case above would only match binary
operations with two equal operands.
Some other examples of guarded patterns are:
// match only positive integers
case n: Int if 0 < n => ...
// match only strings starting with the letter ‘a'
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case s: String if s(0) == 'a' => ...
15.4
Pattern overlaps
Patterns are tried in the order in which they are written. The version of
simplify shown in Listing 15.15 presents an example where the order of
the cases matters.
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def simplifyAll(expr: Expr): Expr = expr match {
case UnOp("-", UnOp("-", e)) =>
simplifyAll(e)
// ‘-' is its own inverse
case BinOp("+", e, Number(0)) =>
simplifyAll(e)
// ‘0' is a neutral element for ‘+'
case BinOp("*", e, Number(1)) =>
simplifyAll(e)
// ‘1' is a neutral element for ‘*'
case UnOp(op, e) =>
UnOp(op, simplifyAll(e))
case BinOp(op, l, r) =>
BinOp(op, simplifyAll(l), simplifyAll(r))
case _ => expr
}
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Listing 15.15 · Match expression in which case order matters.
The version of simplify shown in Listing 15.15 will apply simplification
rules everywhere in an expression, not just at the top, as simplifyTop did.
It can be derived from simplifyTop by adding two more cases for general
unary and binary expressions (cases four and five in Listing 15.15).
The fourth case has the pattern UnOp(op, e); i.e., it matches every unary
operation. The operator and operand of the unary operation can be arbitrary.
They are bound to the pattern variables op and e, respectively. The alternative in this case applies simplifyAll recursively to the operand e and then
rebuilds the same unary operation with the (possibly) simplified operand.
The fifth case for BinOp is analogous: it is a “catch-all” case for arbitrary
binary operations, which recursively applies the simplification method to its
two operands.
In this example, it is important that the catch-all cases come after the
more specific simplification rules. If you wrote them in the other order, then
the catch-all case would be run in favor of the more specific rules. In many
cases, the compiler will even complain if you try. For example, here’s a
match expression that won’t compile because the first case will match anything that would be matched by the second case:
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scala> def simplifyBad(expr: Expr): Expr = expr match {
case UnOp(op, e) => UnOp(op, simplifyBad(e))
case UnOp("-", UnOp("-", e)) => e
}
:21: warning: unreachable code
case UnOp("-", UnOp("-", e)) => e
ˆ
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15.5
Sealed classes
Whenever you write a pattern match, you need to make sure you have covered all of the possible cases. Sometimes you can do this by adding a default
case at the end of the match, but that only applies if there is a sensible default
behavior. What do you do if there is no default? How can you ever feel safe
that you covered all the cases?
You can enlist the help of the Scala compiler in detecting missing combinations of patterns in a match expression. To do this, the compiler needs
to be able to tell which are the possible cases. In general, this is impossible
in Scala because new case classes can be defined at any time and in arbitrary
compilation units. For instance, nothing would prevent you from adding a
fifth case class to the Expr class hierarchy in a different compilation unit
from the one where the other four cases are defined.
The alternative is to make the superclass of your case classes sealed. A
sealed class cannot have any new subclasses added except the ones in the
same file. This is very useful for pattern matching because it means you only
need to worry about the subclasses you already know about. What’s more,
you get better compiler support as well. If you match against case classes
that inherit from a sealed class, the compiler will flag missing combinations
of patterns with a warning message.
If you write a hierarchy of classes intended to be pattern matched, you
should consider sealing them. Simply put the sealed keyword in front of
the class at the top of the hierarchy. Programmers using your class hierarchy
will then feel confident in pattern matching against it. The sealed keyword,
therefore, is often a license to pattern match. Listing 15.16 shows an example
in which Expr is turned into a sealed class.
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sealed abstract class Expr
case class Var(name: String) extends Expr
case class Number(num: Double) extends Expr
case class UnOp(operator: String, arg: Expr) extends Expr
case class BinOp(operator: String,
left: Expr, right: Expr) extends Expr
Listing 15.16 · A sealed hierarchy of case classes.
Now define a pattern match where some of the possible cases are left out:
def describe(e: Expr): String = e match {
case Number(_) => "a number"
case Var(_)
=> "a variable"
}
You will get a compiler warning like the following:
warning: match is not exhaustive!
missing combination
UnOp
missing combination
BinOp
Such a warning tells you that there’s a risk your code might produce a
MatchError exception because some possible patterns (UnOp, BinOp) are
not handled. The warning points to a potential source of runtime faults, so it
is usually a welcome help in getting your program right.
However, at times you might encounter a situation where the compiler is
too picky in emitting the warning. For instance, you might know from the
context that you will only ever apply the describe method above to expressions that are either Numbers or Vars, so you know that no MatchError will
be produced. To make the warning go away, you could add a third catch-all
case to the method, like this:
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def describe(e: Expr): String = e match {
case Number(_) => "a number"
case Var(_) => "a variable"
case _ => throw new RuntimeException // Should not happen
}
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That works, but it is not ideal. You will probably not be very happy that you
were forced to add code that will never be executed (or so you think), just to
make the compiler shut up.
A more lightweight alternative is to add an @unchecked annotation to
the selector expression of the match. This is done as follows:
def describe(e: Expr): String = (e: @unchecked) match {
case Number(_) => "a number"
case Var(_)
=> "a variable"
}
Annotations are described in Chapter 27. In general, you can add an annotation to an expression in the same way you add a type: follow the expression
with a colon and the name of the annotation (preceded by an at sign). For
example, in this case you add an @unchecked annotation to the variable e,
with “e: @unchecked”. The @unchecked annotation has a special meaning
for pattern matching. If a match’s selector expression carries this annotation,
exhaustivity checking for the patterns that follow will be suppressed.
15.6
The Option type
Scala has a standard type named Option for optional values. Such a value
can be of two forms: Some(x), where x is the actual value, or the None
object, which represents a missing value.
Optional values are produced by some of the standard operations on
Scala’s collections. For instance, the get method of Scala’s Map produces
Some(value) if a value corresponding to a given key has been found, or
None if the given key is not defined in the Map. Here’s an example:
scala> val capitals =
Map("France" -> "Paris", "Japan" -> "Tokyo")
capitals: scala.collection.immutable.Map[String,String] =
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Map(France -> Paris, Japan -> Tokyo)
scala> capitals get "France"
res23: Option[String] = Some(Paris)
scala> capitals get "North Pole"
res24: Option[String] = None
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The most common way to take optional values apart is through a pattern
match. For instance:
scala> def show(x: Option[String]) = x match {
case Some(s) => s
case None => "?"
}
show: (x: Option[String])String
scala> show(capitals get "Japan")
res25: String = Tokyo
scala> show(capitals get "France")
res26: String = Paris
scala> show(capitals get "North Pole")
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res27: String = ?
The Option type is used frequently in Scala programs. Compare this to
the dominant idiom in Java of using null to indicate no value. For example,
the get method of java.util.HashMap returns either a value stored in the
HashMap or null if no value was found. This approach works for Java but is
error prone because it is difficult in practice to keep track of which variables
in a program are allowed to be null.
If a variable is allowed to be null, then you must remember to check
it for null every time you use it. When you forget to check, you open the
possibility that a NullPointerException may result at runtime. Because
such exceptions may not happen very often, it can be difficult to discover the
bug during testing. For Scala, the approach would not work at all because it
is possible to store value types in hash maps, and null is not a legal element
for a value type. For instance, a HashMap[Int, Int] cannot return null to
signify “no element.”
By contrast, Scala encourages the use of Option to indicate an optional
value. This approach to optional values has several advantages over Java’s.
First, it is far more obvious to readers of code that a variable whose type
is Option[String] is an optional String than a variable of type String,
which may sometimes be null. But most importantly, that programming
error described earlier of using a variable that may be null without first
checking it for null becomes a type error in Scala. If a variable is of type
Option[String] and you try to use it as a String, your Scala program will
not compile.
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15.7
Chapter 15 · Case Classes and Pattern Matching
Patterns everywhere
Patterns are allowed in many parts of Scala, not just in standalone match
expressions. Take a look at some other places you can use patterns.
Patterns in variable definitions
Anytime you define a val or a var, you can use a pattern instead of a simple
identifier. For example, you can take apart a tuple and assign each of its parts
to its own variable, as shown in Listing 15.17:
scala> val myTuple = (123, "abc")
myTuple: (Int, String) = (123,abc)
scala> val (number, string) = myTuple
number: Int = 123
string: String = abc
Listing 15.17 · Defining multiple variables with one assignment.
This construct is quite useful when working with case classes. If you
know the precise case class you are working with, then you can deconstruct
it with a pattern. Here’s an example:
scala> val exp = new BinOp("*", Number(5), Number(1))
exp: BinOp = BinOp(*,Number(5.0),Number(1.0))
scala> val BinOp(op, left, right) = exp
op: String = *
left: Expr = Number(5.0)
right: Expr = Number(1.0)
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Case sequences as partial functions
A sequence of cases (i.e., alternatives) in curly braces can be used anywhere
a function literal can be used. Essentially, a case sequence is a function
literal, only more general. Instead of having a single entry point and list
of parameters, a case sequence has multiple entry points, each with their
own list of parameters. Each case is an entry point to the function, and the
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parameters are specified with the pattern. The body of each entry point is the
right-hand side of the case.
Here is a simple example:
val withDefault: Option[Int] => Int = {
case Some(x) => x
case None => 0
}
The body of this function has two cases. The first case matches a Some, and
returns the number inside the Some. The second case matches a None, and
returns a default value of zero. Here is this function in use:
scala> withDefault(Some(10))
res28: Int = 10
scala> withDefault(None)
res29: Int = 0
This facility is quite useful for the Akka actors library, because it allows
its receive method to be defined as a series of cases:
var sum = 0
def receive = {
case Data(byte) =>
sum += byte
case GetChecksum(requester) =>
val checksum = ~(sum & 0xFF) + 1
requester ! checksum
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}
One other generalization is worth noting: a sequence of cases gives you a
partial function. If you apply such a function on a value it does not support,
it will generate a run-time exception. For example, here is a partial function
that returns the second element of a list of integers:
val second: List[Int] => Int = {
case x :: y :: _ => y
}
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When you compile this, the compiler will correctly warn that the match is
not exhaustive:
:17: warning: match is not exhaustive!
missing combination
Nil
This function will succeed if you pass it a three-element list, but not if you
pass it an empty list:
scala> second(List(5, 6, 7))
res24: Int = 6
scala> second(List())
scala.MatchError: List()
at $anonfun$1.apply(:17)
at $anonfun$1.apply(:17)
If you want to check whether a partial function is defined, you must
first tell the compiler that you know you are working with partial functions. The type List[Int] => Int includes all functions from lists of integers to integers, whether or not the functions are partial. The type that
only includes partial functions from lists of integers to integers is written
PartialFunction[List[Int],Int]. Here is the second function again,
this time written with a partial function type:
val second: PartialFunction[List[Int],Int] = {
case x :: y :: _ => y
}
Partial functions have a method isDefinedAt, which can be used to test
whether the function is defined at a particular value. In this case, the function
is defined for any list that has at least two elements:
scala> second.isDefinedAt(List(5,6,7))
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res30: Boolean = true
scala> second.isDefinedAt(List())
res31: Boolean = false
The typical example of a partial function is a pattern matching function literal like the one in the previous example. In fact, such an expression gets
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translated by the Scala compiler to a partial function by translating the patterns twice—once for the implementation of the real function, and once to
test whether the function is defined or not.
For instance, the function literal { case x :: y :: _ => y } gets translated
to the following partial function value:
new PartialFunction[List[Int], Int] {
def apply(xs: List[Int]) = xs match {
case x :: y :: _ => y
}
def isDefinedAt(xs: List[Int]) = xs match {
case x :: y :: _ => true
case _ => false
}
}
This translation takes effect whenever the declared type of a function literal
is PartialFunction. If the declared type is just Function1, or is missing,
the function literal is instead translated to a complete function.
In general, you should try to work with complete functions whenever
possible, because using partial functions allows for runtime errors that the
compiler cannot help you with. Sometimes partial functions are really helpful though. You might be sure that an unhandled value will never be supplied.
Alternatively, you might be using a framework that expects partial functions
and so will always check isDefinedAt before calling the function. An example of the latter is the react example given above, where the argument
is a partially defined function, defined precisely for those messages that the
caller wants to handle.
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Patterns in for expressions
You can also use a pattern in a for expression, as shown in Listing 15.18.
This for expression retrieves all key/value pairs from the capitals map.
Each pair is matched against the pattern (country, city), which defines
the two variables country and city.
The pair pattern shown in Listing 15.18 was special because the match
against it can never fail. Indeed, capitals yields a sequence of pairs, so you
can be sure that every generated pair can be matched against a pair pattern.
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scala> for ((country, city) <- capitals)
println("The capital of " + country + " is " + city)
The capital of France is Paris
The capital of Japan is Tokyo
Listing 15.18 · A for expression with a tuple pattern.
But it is equally possible that a pattern might not match a generated value.
Listing 15.19 shows an example where that is the case.
scala> val results = List(Some("apple"), None,
Some("orange"))
results: List[Option[String]] = List(Some(apple), None,
Some(orange))
scala> for (Some(fruit) <- results) println(fruit)
apple
orange
Listing 15.19 · Picking elements of a list that match a pattern.
As you can see from this example, generated values that do not match the
pattern are discarded. For instance, the second element None in the results
list does not match the pattern Some(fruit); therefore it does not show up
in the output.
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15.8
A larger example
After having learned the different forms of patterns, you might be interested
in seeing them applied in a larger example. The proposed task is to write an
expression formatter class that displays an arithmetic expression in a twodimensional layout. Divisions such as “x / (x + 1)” should be printed vertically, by placing the numerator on top of the denominator, like this:
x
----x + 1
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As another example, here’s the expression ((a / (b * c) + 1 / n) / 3) in
two dimensional layout:
a
1
----- + b * c
n
--------3
From these examples it looks like the class (we’ll call it ExprFormatter)
will have to do a fair bit of layout juggling, so it makes sense to use the
layout library developed in Chapter 10. We’ll also use the Expr family of
case classes you saw previously in this chapter, and place both Chapter 10’s
layout library and this chapter’s expression formatter into named packages.
The full code for the example will be shown in Listings 15.20 and 15.21.
A useful first step is to concentrate on horizontal layout. A structured
expression like:
BinOp("+",
BinOp("*",
BinOp("+", Var("x"), Var("y")),
Var("z")),
Number(1))
should print (x + y) * z + 1. Note that parentheses are mandatory around
x + y, but would be optional around (x + y) * z. To keep the layout as
legible as possible, your goal should be to omit parentheses wherever they
are redundant, while ensuring that all necessary parentheses are present.
To know where to put parentheses, the code needs to know about the
relative precedence of each operator, so it’s a good idea to tackle this first.
You could express the relative precedence directly as a map literal of the
following form:
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Map(
"|" -> 0, "||" -> 0,
"&" -> 1, "&&" -> 1, ...
)
However, this would involve some amount of pre-computation of precedences on your part. A more convenient approach is to just define groups
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Section 15.8
Chapter 15 · Case Classes and Pattern Matching
of operators of increasing precedence and then calculate the precedence of
each operator from that. Listing 15.20 shows the code.
The precedence variable is a map from operators to their precedences,
which are integers starting with 0. It is calculated using a for expression with two generators. The first generator produces every index i of
the opGroups array. The second generator produces every operator op in
opGroups(i). For each such operator the for expression yields an association from the operator op to its index i. Hence, the relative position of an
operator in the array is taken to be its precedence.
Associations are written with an infix arrow, e.g., op -> i. So far you
have seen associations only as part of map constructions, but they are also
values in their own right. In fact, the association op -> i is nothing else but
the pair (op, i).
Now that you have fixed the precedence of all binary operators except /,
it makes sense to generalize this concept to also cover unary operators. The
precedence of a unary operator is higher than the precedence of every binary
operator. Thus we can set unaryPrecedence (shown in Listing 15.20) to the
length of the opGroups array, which is one more than the precedence of the *
and % operators. The precedence of a fraction is treated differently from the
other operators because fractions use vertical layout. However, it will prove
convenient to assign to the division operator the special precedence value -1,
so we’ll initialize fractionPrecedence to -1 (shown in Listing 15.20).
After these preparations, you are ready to write the main format method.
This method takes two arguments: an expression e, of type Expr, and the
precedence enclPrec of the operator directly enclosing the expression e. (If
there’s no enclosing operator, enclPrec should be zero.) The method yields
a layout element that represents a two-dimensional array of characters.
Listing 15.21 shows the remainder of class ExprFormatter, which includes three methods. The first method, stripDot, is a helper method. The
next method, the private format method, does most of the work to format expressions. The last method, also named format, is the lone public method in
the library, which takes an expression to format. The private format method
does its work by performing a pattern match on the kind of expression. The
match expression has five cases. We’ll discuss each case individually.
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package org.stairwaybook.expr
import org.stairwaybook.layout.Element.elem
sealed abstract class Expr
case class Var(name: String) extends Expr
case class Number(num: Double) extends Expr
case class UnOp(operator: String, arg: Expr) extends Expr
case class BinOp(operator: String,
left: Expr, right: Expr) extends Expr
class ExprFormatter {
// Contains operators in groups of increasing precedence
private val opGroups =
Array(
Set("|", "||"),
Set("&", "&&"),
Set("ˆ"),
Set("==", "!="),
Set("<", "<=", ">", ">="),
Set("+", "-"),
Set("*", "%")
)
// A mapping from operators to their precedence
private val precedence = {
val assocs =
for {
i <- 0 until opGroups.length
op <- opGroups(i)
} yield op -> i
assocs.toMap
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}
private val unaryPrecedence = opGroups.length
private val fractionPrecedence = -1
// continued in Listing 15.21...
Listing 15.20 · The top half of the expression formatter.
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// ...continued from Listing 15.20
import org.stairwaybook.layout.Element
private def format(e: Expr, enclPrec: Int): Element =
e match {
case Var(name) =>
elem(name)
case Number(num) =>
def stripDot(s: String) =
if (s endsWith ".0") s.substring(0, s.length - 2)
else s
elem(stripDot(num.toString))
case UnOp(op, arg) =>
elem(op) beside format(arg, unaryPrecedence)
case BinOp("/", left, right) =>
val top = format(left, fractionPrecedence)
val bot = format(right, fractionPrecedence)
val line = elem('-', top.width max bot.width, 1)
val frac = top above line above bot
if (enclPrec != fractionPrecedence) frac
else elem(" ") beside frac beside elem(" ")
case BinOp(op, left, right) =>
val opPrec = precedence(op)
val l = format(left, opPrec)
val r = format(right, opPrec + 1)
val oper = l beside elem(" " + op + " ") beside r
if (enclPrec <= opPrec) oper
else elem("(") beside oper beside elem(")")
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}
def format(e: Expr): Element = format(e, 0)
}
Listing 15.21 · The bottom half of the expression formatter.
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The first case is:
case Var(name) =>
elem(name)
If the expression is a variable, the result is an element formed from the variable’s name.
The second case is:
case Number(num) =>
def stripDot(s: String) =
if (s endsWith ".0") s.substring(0, s.length - 2)
else s
elem(stripDot(num.toString))
If the expression is a number, the result is an element formed from the number’s value. The stripDot function cleans up the display of a floating-point
number by stripping any ".0" suffix from a string.
The third case is:
case UnOp(op, arg) =>
elem(op) beside format(arg, unaryPrecedence)
If the expression is a unary operation UnOp(op, arg) the result is formed
from the operation op and the result of formatting the argument arg with the
highest-possible environment precedence.4 This means that if arg is a binary
operation (but not a fraction) it will always be displayed in parentheses.
The fourth case is:
case BinOp("/", left, right) =>
val top = format(left, fractionPrecedence)
val bot = format(right, fractionPrecedence)
val line = elem('-', top.width max bot.width, 1)
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val frac = top above line above bot
if (enclPrec != fractionPrecedence) frac
else elem(" ") beside frac beside elem(" ")
4 The
value of unaryPrecedence is the highest possible precedence, because it was initialized to one more than the precedence of the * and % operators.
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Section 15.8
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If the expression is a fraction, an intermediate result frac is formed by placing the formatted operands left and right on top of each other, separated
by an horizontal line element. The width of the horizontal line is the maximum of the widths of the formatted operands. This intermediate result is
also the final result unless the fraction appears itself as an argument of another fraction. In the latter case, a space is added on each side of frac. To
see the reason why, consider the expression “(a / b) / c”.
Without the widening correction, formatting this expression would give:
a
b
c
The problem with this layout is evident—it’s not clear where the top-level
fractional bar is. The expression above could mean either “(a / b) / c” or
“a / (b / c)”. To disambiguate, a space should be added on each side to the
layout of the nested fraction “a / b”.
Then the layout becomes unambiguous:
a
b
--c
The fifth and last case is:
case BinOp(op, left, right) =>
val opPrec = precedence(op)
val l = format(left, opPrec)
val r = format(right, opPrec + 1)
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val oper = l beside elem(" " + op + " ") beside r
if (enclPrec <= opPrec) oper
else elem("(") beside oper beside elem(")")
This case applies for all other binary operations. Since it comes after the
case starting with:
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337
Section 15.8
Chapter 15 · Case Classes and Pattern Matching
case BinOp("/", left, right) => ...
you know that the operator op in the pattern BinOp(op, left, right) cannot be a division. To format such a binary operation, one needs to format
first its operands left and right. The precedence parameter for formatting
the left operand is the precedence opPrec of the operator op, while for the
right operand it is one more than that. This scheme ensures that parentheses
also reflect the correct associativity.
For instance, the operation:
BinOp("-", Var("a"), BinOp("-", Var("b"), Var("c")))
would be correctly parenthesized as “a - (b - c)”. The intermediate result
oper is then formed by placing the formatted left and right operands sideby-side, separated by the operator. If the precedence of the current operator
is smaller than the precedence of the enclosing operator, oper is placed between parentheses; otherwise, it is returned as is.
import org.stairwaybook.expr._
object Express extends App {
val f = new ExprFormatter
val e1 = BinOp("*", BinOp("/", Number(1), Number(2)),
BinOp("+", Var("x"), Number(1)))
val e2 = BinOp("+", BinOp("/", Var("x"), Number(2)),
BinOp("/", Number(1.5), Var("x")))
val e3 = BinOp("/", e1, e2)
def show(e: Expr) = println(f.format(e)+ "\n\n")
for (e <- Array(e1, e2, e3)) show(e)
}
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Listing 15.22 · An application that prints formatted expressions.
This finishes the design of the private format function. The only remaining method is the public format method, which allows client programmers to format a top-level expression without passing a precedence argument. Listing 15.22 shows a demo program that exercises ExprFormatter.
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Section 15.9
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Note that, even though this program does not define a main method, it
is still a runnable application because it inherits from the App trait. You can
run the Express program with the command:
scala Express
This will give the following output:
1
- * (x + 1)
2
x
1.5
- + --2
x
1
- * (x + 1)
2
----------x
1.5
- + --2
x
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15.9
Conclusion
In this chapter, you learned about Scala’s case classes and pattern matching
in detail. By using them, you can take advantage of several concise idioms
not normally available in object-oriented languages. However, Scala’s pattern matching goes further than this chapter describes. If you want to use
pattern matching on one of your classes, but you do not want to open access
to your classes the way case classes do, you can use the extractors described
in Chapter 26. In the next chapter, we’ll turn our attention to lists.
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339
Chapter 16
Working with Lists
Lists are probably the most commonly used data structure in Scala programs.
This chapter explains lists in detail. We will present many common operations that can be performed on lists. We’ll also cover some important design
principles for programs working on lists.
16.1
List literals
You saw lists already in the preceding chapters, so you know that a list containing the elements 'a', 'b', and 'c' is written List('a', 'b', 'c').
Here are some other examples:
val fruit = List("apples", "oranges", "pears")
val nums = List(1, 2, 3, 4)
val diag3 =
List(
List(1, 0, 0),
List(0, 1, 0),
List(0, 0, 1)
)
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val empty = List()
Lists are quite similar to arrays, but there are two important differences.
First, lists are immutable. That is, elements of a list cannot be changed
by assignment. Second, lists have a recursive structure (i.e., a linked list),1
whereas arrays are flat.
1 For
a graphical depiction of the structure of a List, see Figure 22.2 on page 504.
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Section 16.2
16.2
Chapter 16 · Working with Lists
The List type
Like arrays, lists are homogeneous: the elements of a list all have the same
type. The type of a list that has elements of type T is written List[T]. For
instance, here are the same four lists with explicit types added:
val fruit: List[String] = List("apples", "oranges", "pears")
val nums: List[Int] = List(1, 2, 3, 4)
val diag3: List[List[Int]] =
List(
List(1, 0, 0),
List(0, 1, 0),
List(0, 0, 1)
)
val empty: List[Nothing] = List()
The list type in Scala is covariant. This means that for each pair of
types S and T, if S is a subtype of T, then List[S] is a subtype of List[T].
For instance, List[String] is a subtype of List[Object]. This is natural
because every list of strings can also be seen as a list of objects.2
Note that the empty list has type List[Nothing]. You saw in Section 11.3 that Nothing is the bottom type in Scala’s class hierarchy. It is
a subtype of every other Scala type. Because lists are covariant, it follows
that List[Nothing] is a subtype of List[T] for any type T. So the empty
list object, which has type List[Nothing], can also be seen as an object
of every other list type of the form List[T]. That’s why it is permissible to
write code like:
// List() is also of type List[String]!
val xs: List[String] = List()
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16.3
Constructing lists
All lists are built from two fundamental building blocks, Nil and :: (pronounced “cons”). Nil represents the empty list. The infix operator, ::,
expresses list extension at the front. That is, x :: xs represents a list whose
2 Chapter
19 gives more details on covariance and other kinds of variance.
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Section 16.4
Chapter 16 · Working with Lists
first element is x, followed by (the elements of) list xs. Hence, the previous
list values could also have been defined as follows:
val fruit = "apples" :: ("oranges" :: ("pears" :: Nil))
val nums
= 1 :: (2 :: (3 :: (4 :: Nil)))
val diag3 = (1 :: (0 :: (0 :: Nil))) ::
(0 :: (1 :: (0 :: Nil))) ::
(0 :: (0 :: (1 :: Nil))) :: Nil
val empty = Nil
In fact the previous definitions of fruit, nums, diag3, and empty in terms of
List(...) are just wrappers that expand to these definitions. For instance,
List(1, 2, 3) creates the list 1 :: (2 :: (3 :: Nil)).
Because it ends in a colon, the :: operation associates to the right:
A :: B :: C is interpreted as A :: (B :: C). Therefore, you can drop the
parentheses in the previous definitions. For instance:
val nums = 1 :: 2 :: 3 :: 4 :: Nil
is equivalent to the previous definition of nums.
16.4
Basic operations on lists
All operations on lists can be expressed in terms of the following three:
head
tail
isEmpty
returns the first element of a list
returns a list consisting of all elements except the first
returns true if the list is empty
These operations are defined as methods of class List. Some examples are
shown in Table 16.1. The head and tail methods are defined only for nonempty lists. When selected from an empty list, they throw an exception:
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scala> Nil.head
java.util.NoSuchElementException: head of empty list
As an example of how lists can be processed, consider sorting the elements of
a list of numbers into ascending order. One simple way to do so is insertion
sort, which works as follows: To sort a non-empty list x :: xs, sort the
remainder xs and insert the first element x at the right position in the result.
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Table 16.1 · Basic list operations
What it is
empty.isEmpty
fruit.isEmpty
fruit.head
fruit.tail.head
diag3.head
What it does
returns true
returns false
returns "apples"
returns "oranges"
returns List(1, 0, 0)
Sorting an empty list yields the empty list. Expressed as Scala code, the
insertion sort algorithm looks like:
def isort(xs: List[Int]): List[Int] =
if (xs.isEmpty) Nil
else insert(xs.head, isort(xs.tail))
def insert(x: Int, xs: List[Int]): List[Int] =
if (xs.isEmpty || x <= xs.head) x :: xs
else xs.head :: insert(x, xs.tail)
16.5
List patterns
Lists can also be taken apart using pattern matching. List patterns correspond
one-by-one to list expressions. You can either match on all elements of a list
using a pattern of the form List(...), or you take lists apart bit by bit using
patterns composed from the :: operator and the Nil constant.
Here’s an example of the first kind of pattern:
scala> val List(a, b, c) = fruit
a: String = apples
b: String = oranges
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c: String = pears
The pattern List(a, b, c) matches lists of length 3, and binds the three
elements to the pattern variables a, b, and c. If you don’t know the number
of list elements beforehand, it’s better to match with :: instead. For instance,
the pattern a :: b :: rest matches lists of length 2 or greater:
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About pattern matching on Lists
If you review the possible forms of patterns explained in Chapter 15,
you might find that neither List(...) nor :: look like it fits the
kinds of patterns defined there. In fact, List(...) is an instance of
a library-defined extractor pattern. Such patterns will be discussed in
Chapter 26. The “cons” pattern x :: xs is a special case of an infix
operation pattern. As an expression, an infix operation is equivalent
to a method call. For patterns, the rules are different: As a pattern, an
infix operation such as p op q is equivalent to op(p, q). That is, the
infix operator op is treated as a constructor pattern. In particular, a cons
pattern such as x :: xs is treated as ::(x, xs).
This hints that there should be a class named :: that corresponds
to the pattern constructor. Indeed, there is such a class—it is named
scala.:: and is exactly the class that builds non-empty lists. So ::
exists twice in Scala, once as a name of a class in package scala and
again as a method in class List. The effect of the method :: is to
produce an instance of the class scala.::. You’ll find out more details
about how the List class is implemented in Chapter 22.
scala> val a :: b :: rest = fruit
a: String = apples
b: String = oranges
rest: List[String] = List(pears)
Taking lists apart with patterns is an alternative to taking them apart with the
basic methods head, tail, and isEmpty. For instance, here’s insertion sort
again, this time written with pattern matching:
def isort(xs: List[Int]): List[Int] = xs match {
case List()
=> List()
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case x :: xs1 => insert(x, isort(xs1))
}
def insert(x: Int, xs: List[Int]): List[Int] = xs match {
case List()
=> List(x)
case y :: ys => if (x <= y) x :: xs
else y :: insert(x, ys)
}
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Chapter 16 · Working with Lists
Often, pattern matching over lists is clearer than decomposing them with
methods, so pattern matching should be a part of your list processing toolbox.
This is all you need to know about lists in Scala to use them correctly.
However, there are also a large number of methods that capture common patterns of operations over lists. These methods make list processing programs
more concise and often clearer. The next two sections present the most important methods defined in the List class.
16.6
First-order methods on class List
This section explains most first-order methods defined in the List class.
A method is first-order if it does not take any functions as arguments. We
will also introduce some recommended techniques to structure programs that
operate on lists by using two examples.
Concatenating two lists
An operation similar to :: is list concatenation, written ‘:::’. Unlike ::,
::: takes two lists as operands. The result of xs ::: ys is a new list that
contains all the elements of xs, followed by all the elements of ys.
Here are some examples:
scala> List(1, 2) ::: List(3, 4, 5)
res0: List[Int] = List(1, 2, 3, 4, 5)
scala> List() ::: List(1, 2, 3)
res1: List[Int] = List(1, 2, 3)
scala> List(1, 2, 3) ::: List(4)
res2: List[Int] = List(1, 2, 3, 4)
Like cons, list concatenation associates to the right. An expression like this:
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xs ::: ys ::: zs
is interpreted like this:
xs ::: (ys ::: zs)
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The Divide and Conquer principle
Concatenation (:::) is implemented as a method in class List. It would also
be possible to implement concatenation “by hand,” using pattern matching
on lists. It’s instructive to try to do that yourself, because it shows a common
way to implement algorithms using lists. First, we’ll settle on a signature
for the concatenation method, which we’ll call append. In order not to mix
things up too much, assume that append is defined outside the List class,
so it will take the two lists to be concatenated as parameters. These two
lists must agree on their element type, but that element type can be arbitrary.
This can be expressed by giving append a type parameter3 that represents
the element type of the two input lists:
def append[T](xs: List[T], ys: List[T]): List[T]
To design the implementation of append, it pays to remember the “divide
and conquer” design principle for programs over recursive data structures
such as lists. Many algorithms over lists first split an input list into simpler
cases using a pattern match. That’s the divide part of the principle. They
then construct a result for each case. If the result is a non-empty list, some of
its parts may be constructed by recursive invocations of the same algorithm.
That’s the conquer part of the principle.
To apply this principle to the implementation of the append method, the
first question to ask is on which list to match. This is less trivial in the case of
append than for many other methods because there are two choices. However, the subsequent “conquer” phase tells you that you need to construct a
list consisting of all elements of both input lists. Since lists are constructed
from the back towards the front, ys can remain intact, whereas xs will need
to be taken apart and prepended to ys. Thus, it makes sense to concentrate
on xs as a source for a pattern match. The most common pattern match over
lists simply distinguishes an empty from a non-empty list. So this gives the
following outline of an append method:
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def append[T](xs: List[T], ys: List[T]): List[T] =
xs match {
case List() => ???
case x :: xs1 => ???
}
3 Type
parameters will be explained in more detail in Chapter 19.
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All that remains is to fill in the two places marked with ???.4 The first
such place is the alternative where the input list xs is empty. In this case
concatenation yields the second list:
case List() => ys
The second place left open is the alternative where the input list xs consists
of some head x followed by a tail xs1. In this case the result is also a nonempty list. To construct a non-empty list you need to know what the head
and the tail of that list should be. You know that the first element of the result
list is x. As for the remaining elements, these can be computed by appending
the second list, ys, to the rest of the first list, xs1.
This completes the design and gives:
def append[T](xs: List[T], ys: List[T]): List[T] =
xs match {
case List() => ys
case x :: xs1 => x :: append(xs1, ys)
}
The computation of the second alternative illustrated the “conquer” part of
the divide and conquer principle: Think first what the shape of the desired
output should be, then compute the individual parts of that shape, using recursive invocations of the algorithm where appropriate. Finally, construct
the output from these parts.
Taking the length of a list: length
The length method computes the length of a list.
scala> List(1, 2, 3).length
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res3: Int = 3
On lists, unlike arrays, length is a relatively expensive operation. It needs to
traverse the whole list to find its end, and therefore takes time proportional to
the number of elements in the list. That’s why it’s not a good idea to replace
a test such as xs.isEmpty by xs.length == 0. The result of the two tests is
equivalent, but the second one is slower, in particular if the list xs is long.
4 The ??? method, which throws scala.NotImplementedError and has result type
Nothing, can be used as a temporary implementation during development.
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Chapter 16 · Working with Lists
Accessing the end of a list: init and last
You know already the basic operations head and tail, which respectively
take the first element of a list, and the rest of the list except the first element.
They each have a dual operation: last returns the last element of a (nonempty) list, whereas init returns a list consisting of all elements except the
last one:
scala> val abcde = List('a', 'b', 'c', 'd', 'e')
abcde: List[Char] = List(a, b, c, d, e)
scala> abcde.last
res4: Char = e
scala> abcde.init
res5: List[Char] = List(a, b, c, d)
Like head and tail, these methods throw an exception when applied to an
empty list:
scala> List().init
java.lang.UnsupportedOperationException: Nil.init
at scala.List.init(List.scala:544)
at ...
scala> List().last
java.util.NoSuchElementException: Nil.last
at scala.List.last(List.scala:563)
at ...
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Unlike head and tail, which both run in constant time, init and last need
to traverse the whole list to compute their result. As a result, they take time
proportional to the length of the list.
It’s a good idea to organize your data so that most accesses
are at the head of a list, rather than the last element.
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Reversing lists: reverse
If at some point in the computation an algorithm demands frequent accesses
to the end of a list, it’s sometimes better to reverse the list first and work with
the result instead. Here’s how to do the reversal:
scala> abcde.reverse
res6: List[Char] = List(e, d, c, b, a)
Like all other list operations, reverse creates a new list rather than changing
the one it operates on. Since lists are immutable, such a change would not
be possible anyway. To verify this, check that the original value of abcde is
unchanged after the reverse operation:
scala> abcde
res7: List[Char] = List(a, b, c, d, e)
The reverse, init, and last operations satisfy some laws that can be used
for reasoning about computations and for simplifying programs.
1. reverse is its own inverse:
xs.reverse.reverse
equals
xs
2. reverse turns init to tail and last to head, except that the elements are reversed:
xs.reverse.init
xs.reverse.tail
xs.reverse.head
xs.reverse.last
equals
equals
equals
equals
xs.tail.reverse
xs.init.reverse
xs.last
xs.head
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Reverse could be implemented using concatenation (:::), like in the following method, rev:
def rev[T](xs: List[T]): List[T] = xs match {
case List() => xs
case x :: xs1 => rev(xs1) ::: List(x)
}
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However, this method is less efficient than one would hope for. To study
the complexity of rev, assume that the list xs has length n. Notice that
there are n recursive calls to rev. Each call except the last involves a list
concatenation. List concatenation xs ::: ys takes time proportional to the
length of its first argument xs. Hence, the total complexity of rev is:
n + (n − 1) + ... + 1 = (1 + n) ∗ n/2
In other words, rev’s complexity is quadratic in the length of its input argument. This is disappointing when compared to the standard reversal of a
mutable, linked list, which has linear complexity. However, the current implementation of rev is not the best implementation possible. In the example
starting on page 363, you will see how to speed it up.
Prefixes and suffixes: drop, take, and splitAt
The drop and take operations generalize tail and init in that they return
arbitrary prefixes or suffixes of a list. The expression “xs take n” returns
the first n elements of the list xs. If n is greater than xs.length, the whole
list xs is returned. The operation “xs drop n” returns all elements of the list
xs, except for the first n ones. If n is greater than xs.length, the empty list
is returned.
The splitAt operation splits the list at a given index, returning a pair of
two lists.5 It is defined by the equality:
xs splitAt n
equals (xs take n, xs drop n)
However, splitAt avoids traversing the list xs twice. Here are some examples of these three methods:
scala> abcde take 2
res8: List[Char] = List(a, b)
scala> abcde drop 2
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res9: List[Char] = List(c, d, e)
scala> abcde splitAt 2
res10: (List[Char], List[Char]) = (List(a, b),List(c, d, e))
5 As
mentioned in Section 10.12, the term pair is an informal name for Tuple2.
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Element selection: apply and indices
Random element selection is supported through the apply method; however
it is a less common operation for lists than it is for arrays.
scala> abcde apply 2 // rare in Scala
res11: Char = c
As for all other types, apply is implicitly inserted when an object appears in
the function position in a method call. So the line above can be shortened to:
scala> abcde(2)
// rare in Scala
res12: Char = c
One reason why random element selection is less popular for lists than for
arrays is that xs(n) takes time proportional to the index n. In fact, apply is
simply defined by a combination of drop and head:
xs apply n
equals (xs drop n).head
This definition also makes clear that list indices range from 0 up to the length
of the list minus one, the same as for arrays. The indices method returns a
list consisting of all valid indices of a given list:
scala> abcde.indices
res13: scala.collection.immutable.Range
= Range(0, 1, 2, 3, 4)
Flattening a list of lists: flatten
The flatten method takes a list of lists and flattens it out to a single list:
scala> List(List(1, 2), List(3), List(), List(4, 5)).flatten
res14: List[Int] = List(1, 2, 3, 4, 5)
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scala> fruit.map(_.toCharArray).flatten
res15: List[Char] = List(a, p, p, l, e, s, o, r, a, n, g, e,
s, p, e, a, r, s)
It can only be applied to lists whose elements are all lists. Trying to
flatten any other list will give a compilation error:
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scala> List(1, 2, 3).flatten
:8: error: No implicit view available from Int =>
scala.collection.GenTraversableOnce[B].
List(1, 2, 3).flatten
ˆ
Zipping lists: zip and unzip
The zip operation takes two lists and forms a list of pairs:
scala> abcde.indices zip abcde
res17: scala.collection.immutable.IndexedSeq[(Int, Char)] =
Vector((0,a), (1,b), (2,c), (3,d), (4,e))
If the two lists are of different length, any unmatched elements are dropped:
scala> val zipped = abcde zip List(1, 2, 3)
zipped: List[(Char, Int)] = List((a,1), (b,2), (c,3))
A useful special case is to zip a list with its index. This is done most efficiently with the zipWithIndex method, which pairs every element of a list
with the position where it appears in the list.
scala> abcde.zipWithIndex
res18: List[(Char, Int)] = List((a,0), (b,1), (c,2), (d,3),
(e,4))
Any list of tuples can also be changed back to a tuple of lists by using the
unzip method:
scala> zipped.unzip
res19: (List[Char], List[Int])
Prepared for Library Genesis
= (List(a, b, c),List(1, 2, 3))
The zip and unzip methods provide one way to operate on multiple lists
together. See Section 16.9 for a more concise way to do this.
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Displaying lists: toString and mkString
The toString operation returns the canonical string representation of a list:
scala> abcde.toString
res20: String = List(a, b, c, d, e)
If you want a different representation you can use the mkString method. The
operation xs mkString (pre, sep, post) involves four operands: the list
xs to be displayed, a prefix string pre to be displayed in front of all elements,
a separator string sep to be displayed between successive elements, and a
postfix string post to be displayed at the end.
The result of the operation is the string:
pre + xs(0) + sep + . . . + sep + xs(xs.length - 1) + post
The mkString method has two overloaded variants that let you drop some
or all of its arguments. The first variant only takes a separator string:
xs mkString sep
equals xs mkString ("", sep, "")
The second variant lets you omit all arguments:
xs.mkString
equals xs mkString ""
Here are some examples:
scala> abcde mkString ("[", ",", "]")
res21: String = [a,b,c,d,e]
scala> abcde mkString ""
res22: String = abcde
scala> abcde.mkString
res23: String = abcde
scala> abcde mkString ("List(", ", ", ")")
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res24: String = List(a, b, c, d, e)
There are also variants of the mkString methods called addString which
append the constructed string to a StringBuilder object,6 rather than returning them as a result:
6 This
is class scala.StringBuilder, not java.lang.StringBuilder.
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scala> val buf = new StringBuilder
buf: StringBuilder =
scala> abcde addString (buf, "(", ";", ")")
res25: StringBuilder = (a;b;c;d;e)
The mkString and addString methods are inherited from List’s super trait
Traversable, so they are applicable to all other collections as well.
Converting lists: iterator, toArray, copyToArray
To convert data between the flat world of arrays and the recursive world of
lists, you can use method toArray in class List and toList in class Array:
scala> val arr = abcde.toArray
arr: Array[Char] = Array(a, b, c, d, e)
scala> arr.toList
res26: List[Char] = List(a, b, c, d, e)
There’s also a method copyToArray, which copies list elements to successive array positions within some destination array. The operation:
xs copyToArray (arr, start)
copies all elements of the list xs to the array arr, beginning with position
start. You must ensure that the destination array arr is large enough to
hold the list in full. Here’s an example:
scala> val arr2 = new Array[Int](10)
arr2: Array[Int] = Array(0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
scala> List(1, 2, 3) copyToArray (arr2, 3)
scala> arr2
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res28: Array[Int] = Array(0, 0, 0, 1, 2, 3, 0, 0, 0, 0)
Finally, if you need to access list elements via an iterator, you can use the
iterator method:
scala> val it = abcde.iterator
it: Iterator[Char] = non-empty iterator
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scala> it.next
res29: Char = a
scala> it.next
res30: Char = b
Example: Merge sort
The insertion sort presented earlier is concise to write, but it is not very
efficient. Its average complexity is proportional to the square of the length
of the input list. A more efficient algorithm is merge sort.
The fast track
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This example provides another illustration of the divide and conquer
principle and currying, as well as a useful discussion of algorithmic
complexity. If you prefer to move a bit faster on your first pass through
this book, however, you can safely skip to Section 16.7.
Merge sort works as follows: First, if the list has zero or one elements, it
is already sorted, so the list can be returned unchanged. Longer lists are split
into two sub-lists, each containing about half the elements of the original
list. Each sub-list is sorted by a recursive call to the sort function, and the
resulting two sorted lists are then combined in a merge operation.
For a general implementation of merge sort, you want to leave open the
type of list elements to be sorted and the function to be used for the comparison of elements. You obtain a function of maximal generality by passing
these two items as parameters. This leads to the implementation shown in
Listing 16.1.
The complexity of msort is order (n log(n)), where n is the length of
the input list. To see why, note that splitting a list in two and merging two
sorted lists each take time proportional to the length of the argument list(s).
Each recursive call of msort halves the number of elements in its input, so
there are about log(n) consecutive recursive calls until the base case of lists
of length 1 is reached. However, for longer lists each call spawns off two
further calls. Adding everything up, we obtain at each of the log(n) call
levels, every element of the original lists takes part in one split operation and
one merge operation.
Hence, every call level has a total cost proportional to n. Since there are
log(n) call levels, we obtain an overall cost proportional to n log(n). That
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def msort[T](less: (T, T) => Boolean)
(xs: List[T]): List[T] = {
def merge(xs: List[T], ys: List[T]): List[T] =
(xs, ys) match {
case (Nil, _) => ys
case (_, Nil) => xs
case (x :: xs1, y :: ys1) =>
if (less(x, y)) x :: merge(xs1, ys)
else y :: merge(xs, ys1)
}
val n = xs.length / 2
if (n == 0) xs
else {
val (ys, zs) = xs splitAt n
merge(msort(less)(ys), msort(less)(zs))
}
}
Listing 16.1 · A merge sort function for Lists.
cost does not depend on the initial distribution of elements in the list, so the
worst case cost is the same as the average case cost. This property makes
merge sort an attractive algorithm for sorting lists.
Here is an example of how msort is used:
scala> msort((x: Int, y: Int) => x < y)(List(5, 7, 1, 3))
res31: List[Int] = List(1, 3, 5, 7)
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The msort function is a classical example of the currying concept discussed in Section 9.3. Currying makes it easy to specialize the function for
particular comparison functions. Here’s an example:
scala> val intSort = msort((x: Int, y: Int) => x < y) _
intSort: List[Int] => List[Int] =
The intSort variable refers to a function that takes a list of integers and
sorts them in numerical order. As described in Section 8.6, an underscore
stands for a missing argument list. In this case, the missing argument is the
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list that should be sorted. As another example, here’s how you could define
a function that sorts a list of integers in reverse numerical order:
scala> val reverseIntSort = msort((x: Int, y: Int) => x > y) _
reverseIntSort: (List[Int]) => List[Int] =
Because you provided the comparison function already via currying, you
now need only provide the list to sort when you invoke the intSort or
reverseIntSort functions. Here are some examples:
scala> val mixedInts = List(4, 1, 9, 0, 5, 8, 3, 6, 2, 7)
mixedInts: List[Int] = List(4, 1, 9, 0, 5, 8, 3, 6, 2, 7)
scala> intSort(mixedInts)
res0: List[Int] = List(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
scala> reverseIntSort(mixedInts)
res1: List[Int] = List(9, 8, 7, 6, 5, 4, 3, 2, 1, 0)
16.7
Higher-order methods on class List
Many operations over lists have a similar structure. Several patterns appear
time and time again. Some examples are: transforming every element of a
list in some way, verifying whether a property holds for all elements of a list,
extracting from a list elements satisfying a certain criterion, or combining
the elements of a list using some operator. In Java, such patterns would
usually be expressed by idiomatic combinations of for or while loops. In
Scala, they can be expressed more concisely and directly using higher-order
operators,7 which are implemented as methods in class List. These higherorder operators are discussed in this section.
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Mapping over lists: map, flatMap and foreach
The operation xs map f takes as operands a list xs of type List[T] and a
function f of type T => U. It returns the list that results from applying the
function f to each list element in xs. For instance:
7 By
higher-order operators, we mean higher-order functions used in operator notation.
As mentioned in Section 9.1, a function is “higher-order” if it takes one or more other functions as a parameters.
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scala> List(1, 2, 3) map (_ + 1)
res32: List[Int] = List(2, 3, 4)
scala> val words = List("the", "quick", "brown", "fox")
words: List[String] = List(the, quick, brown, fox)
scala> words map (_.length)
res33: List[Int] = List(3, 5, 5, 3)
scala> words map (_.toList.reverse.mkString)
res34: List[String] = List(eht, kciuq, nworb, xof)
The flatMap operator is similar to map, but it takes a function returning a list
of elements as its right operand. It applies the function to each list element
and returns the concatenation of all function results. The difference between
map and flatMap is illustrated in the following example:
scala> words map (_.toList)
res35: List[List[Char]] = List(List(t, h, e), List(q, u, i,
c, k), List(b, r, o, w, n), List(f, o, x))
scala> words flatMap (_.toList)
res36: List[Char] = List(t, h, e, q, u, i, c, k, b, r, o, w,
n, f, o, x)
You see that where map returns a list of lists, flatMap returns a single list in
which all element lists are concatenated.
The differences and interplay between map and flatMap are also demonstrated by the following expression, which constructs a list of all pairs (i, j)
such that 1 ≤ j < i < 5:
scala> List.range(1, 5) flatMap (
i => List.range(1, i) map (j => (i, j))
)
res37: List[(Int, Int)] = List((2,1), (3,1), (3,2), (4,1),
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(4,2), (4,3))
List.range is a utility method that creates a list of all integers in some
range. It is used twice in this example: once to generate a list of integers
from 1 (including) until 5 (excluding), and a second time to generate a list of
integers from 1 until i, for each value of i taken from the first list. The map in
this expression generates a list of tuples (i, j) where j < i. The outer flatMap
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in this example generates this list for each i between 1 and 5, and then concatenates all the results. Alternatively, the same list can be constructed with
a for expression:
for (i <- List.range(1, 5); j <- List.range(1, i)) yield (i, j)
You’ll learn more about the interplay of for expressions and list operations
in Chapter 23.
The third map-like operation is foreach. Unlike map and flatMap, however, foreach takes a procedure (a function with result type Unit) as right
operand. It simply applies the procedure to each list element. The result
of the operation itself is again Unit; no list of results is assembled. As an
example, here is a concise way of summing up all numbers in a list:
scala> var sum = 0
sum: Int = 0
scala> List(1, 2, 3, 4, 5) foreach (sum += _)
scala> sum
res39: Int = 15
Filtering lists: filter, partition, find, takeWhile, dropWhile, and
span
The operation “xs filter p” takes as operands a list xs of type List[T] and
a predicate function p of type T => Boolean. It yields the list of all elements
x in xs for which p(x) is true. For instance:
scala> List(1, 2, 3, 4, 5) filter (_ % 2 == 0)
res40: List[Int] = List(2, 4)
scala> words filter (_.length == 3)
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res41: List[String] = List(the, fox)
The partition method is like filter but returns a pair of lists. One list contains all elements for which the predicate is true, while the other contains all
elements for which the predicate is false. It is defined by the equality:
xs partition p
equals (xs filter p, xs filter (!p(_)))
Here’s an example:
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scala> List(1, 2, 3, 4, 5) partition (_ % 2 == 0)
res42: (List[Int], List[Int]) = (List(2, 4),List(1, 3, 5))
The find method is also similar to filter, but it returns the first element
satisfying a given predicate, rather than all such elements. The operation
xs find p takes a list xs and a predicate p as operands. It returns an optional
value. If there is an element x in xs for which p(x) is true, Some(x) is
returned. Otherwise, p is false for all elements, and None is returned. Here
are some examples:
scala>
List(1, 2, 3, 4, 5) find (_ % 2 == 0)
res43: Option[Int] = Some(2)
scala>
List(1, 2, 3, 4, 5) find (_ <= 0)
res44: Option[Int] = None
The takeWhile and dropWhile operators also take a predicate as their right
operand. The operation xs takeWhile p takes the longest prefix of list xs
such that every element in the prefix satisfies p. Analogously, the operation
xs dropWhile p removes the longest prefix from list xs such that every
element in the prefix satisfies p. Here are some examples:
scala> List(1, 2, 3, -4, 5) takeWhile (_ > 0)
res45: List[Int] = List(1, 2, 3)
scala> words dropWhile (_ startsWith "t")
res46: List[String] = List(quick, brown, fox)
The span method combines takeWhile and dropWhile in one operation,
just like splitAt combines take and drop. It returns a pair of two lists,
defined by the equality:
xs span p
equals (xs takeWhile p, xs dropWhile p)
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Like splitAt, span avoids traversing the list xs twice:
scala> List(1, 2, 3, -4, 5) span (_ > 0)
res47: (List[Int], List[Int]) = (List(1, 2, 3),List(-4, 5))
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Predicates over lists: forall and exists
The operation xs forall p takes as arguments a list xs and a predicate p. Its
result is true if all elements in the list satisfy p. Conversely, the operation
xs exists p returns true if there is an element in xs that satisfies the predicate p. For instance, to find out whether a matrix represented as a list of lists
has a row with only zeroes as elements:
scala> def hasZeroRow(m: List[List[Int]]) =
m exists (row => row forall (_ == 0))
hasZeroRow: (m: List[List[Int]])Boolean
scala> hasZeroRow(diag3)
res48: Boolean = false
Folding lists: /: and :\
Another common kind of operation combines the elements of a list with
some operator. For instance:
sum(List(a, b, c))
equals 0 + a + b + c
This is a special instance of a fold operation:
scala> def sum(xs: List[Int]): Int = (0 /: xs) (_ + _)
sum: (xs: List[Int])Int
Similarly:
product(List(a, b, c))
equals 1 * a * b * c
is a special instance of this fold operation:
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scala> def product(xs: List[Int]): Int = (1 /: xs) (_ * _)
product: (xs: List[Int])Int
A fold left operation “(z /: xs) (op)” involves three objects: a start value
z, a list xs, and a binary operation op. The result of the fold is op applied
between successive elements of the list prefixed by z. For instance:
(z /: List(a, b, c)) (op)
equals
op(op(op(z, a), b), c)
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Or, graphically:
op
op
op
c
b
z
a
Here’s another example that illustrates how /: is used. To concatenate
all words in a list of strings with spaces between them and in front, you can
write this:
scala>
("" /: words) (_ + " " + _)
res49: String = " the quick brown fox"
This gives an extra space at the beginning. To remove the space, you can use
this slight variation:
scala> (words.head /: words.tail)
(_ + " " + _)
res50: String = the quick brown fox
The /: operator produces left-leaning operation trees (its syntax with the
slash rising forward is intended to be a reflection of that). The operator has
:\ as an analog that produces right-leaning trees. For instance:
equals
(List(a, b, c) :\ z) (op)
Or, graphically:
op
a
op
b
op
c
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op(a, op(b, op(c, z)))
z
The :\ operator is pronounced fold right. It involves the same three
operands as fold left, but the first two appear in reversed order: The first
operand is the list to fold, the second is the start value.
For associative operations, fold left and fold right are equivalent, but
there might be a difference in efficiency. Consider for instance an operation
corresponding to the flatten method, which concatenates all elements in a
list of lists. This could be implemented with either fold left or fold right:
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def flattenLeft[T](xss: List[List[T]]) =
(List[T]() /: xss) (_ ::: _)
def flattenRight[T](xss: List[List[T]]) =
(xss :\ List[T]()) (_ ::: _)
Because list concatenation, xs ::: ys, takes time proportional to its first
argument xs, the implementation in terms of fold right in flattenRight
is more efficient than the fold left implementation in flattenLeft. The
problem is that flattenLeft(xss) copies the first element list xss.head
n − 1 times, where n is the length of the list xss.
Note that both versions of flatten require a type annotation on the
empty list that is the start value of the fold. This is due to a limitation in
Scala’s type inferencer, which fails to infer the correct type of the list automatically. If you try to leave out the annotation, you get the following:
scala> def flattenRight[T](xss: List[List[T]]) =
(xss :\ List()) (_ ::: _)
:8: error: type mismatch;
found
: List[T]
required: List[Nothing]
(xss :\ List()) (_ ::: _)
ˆ
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To find out why the type inferencer goes wrong, you’ll need to know about
the types of the fold methods and how they are implemented. More on this
in Section 16.10.
Lastly, although the /: and :\ operators have the advantage that the
direction of the slash resembles the graphical depiction of their respective
left or right-leaning trees, and the associativity of the colon character places
the start value in the same position in the expression as it is in the tree, some
may find the resulting code less than intuitive. If you prefer, you can use the
methods named foldLeft and foldRight instead, which are also defined
on class List.
Example: List reversal using fold
Earlier in the chapter you saw an implementation of method reverse, named
rev, whose running time was quadratic in the length of the list to be reversed.
Here is a different implementation of reverse that has linear cost. The idea
is to use a fold left operation based on the following scheme:
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def reverseLeft[T](xs: List[T]) = (startvalue /: xs)(operation)
What remains is to fill in the startvalue and operation parts. In fact, you can
try to deduce these parts from some simple examples. To deduce the correct
value of startvalue, you can start with the smallest possible list, List(), and
calculate as follows:
List()
equals (by the properties of reverseLeft)
reverseLeft(List())
equals (by the template for reverseLeft)
(startvalue /: List())(operation)
equals (by the definition of /:)
startvalue
Hence, startvalue must be List(). To deduce the second operand, you
can pick the next smallest list as an example case. You know already that
startvalue is List(), so you can calculate as follows:
List(x)
equals (by the properties of reverseLeft)
reverseLeft(List(x))
equals (by the template for reverseLeft, with startvalue = List())
(List() /: List(x)) (operation)
equals (by the definition of /:)
operation(List(), x)
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Hence, operation(List(), x) equals List(x), which can also be written
as x :: List(). This suggests taking as operation the :: operator with its
operands exchanged. (This operation is sometimes called “snoc,” in reference to ::, which is called cons.) We arrive then at the following implementation for reverseLeft:
def reverseLeft[T](xs: List[T]) =
(List[T]() /: xs) {(ys, y) => y :: ys}
Again, the type annotation in List[T]() is needed to make the type inferencer work. If you analyze the complexity of reverseLeft, you’ll find that
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it applies a constant-time operation (“snoc”) n times, where n is the length
of the argument list. Thus, the complexity of reverseLeft is linear.
Sorting lists: sortWith
The operation xs sortWith before, where “xs” is a list and “before” is a
function that can be used to compare two elements, sorts the elements of list
xs. The expression x before y should return true if x should come before
y in the intended ordering for the sort. For instance:
scala> List(1, -3, 4, 2, 6) sortWith (_ < _)
res51: List[Int] = List(-3, 1, 2, 4, 6)
scala> words sortWith (_.length > _.length)
res52: List[String] = List(quick, brown, the, fox)
Note that sortWith performs a merge sort similar to the msort algorithm
shown in the last section. But sortWith is a method of class List, whereas
msort is defined outside lists.
16.8
Methods of the List object
So far, all operations you have seen in this chapter are implemented as methods of class List, so you invoke them on individual list objects. There are
also a number of methods in the globally accessible object scala.List,
which is the companion object of class List. Some of these operations are
factory methods that create lists. Others are operations that work on lists of
some specific shape. Both kinds of methods will be presented in this section.
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Creating lists from their elements: List.apply
You’ve already seen on several occasions list literals such as List(1, 2, 3).
There’s nothing special about their syntax. A literal like List(1, 2, 3) is
simply the application of the object List to the elements 1, 2, 3. That is, it
is equivalent to List.apply(1, 2, 3):
scala> List.apply(1, 2, 3)
res53: List[Int] = List(1, 2, 3)
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Creating a range of numbers: List.range
The range method, which you saw briefly earlier in the discussion of map
and flatmap, creates a list consisting of a range of numbers. Its simplest
form is List.range(from, until), which creates a list of all numbers starting at from and going up to until minus one. So the end value, until, does
not form part of the range.
There’s also a version of range that takes a step value as third parameter. This operation will yield list elements that are step values apart, starting
at from. The step can be positive or negative:
scala> List.range(1, 5)
res54: List[Int] = List(1, 2, 3, 4)
scala> List.range(1, 9, 2)
res55: List[Int] = List(1, 3, 5, 7)
scala> List.range(9, 1, -3)
res56: List[Int] = List(9, 6, 3)
Creating uniform lists: List.fill
The fill method creates a list consisting of zero or more copies of the same
element. It takes two parameters: the length of the list to be created, and the
element to be repeated. Each parameter is given in a separate list:
scala> List.fill(5)('a')
res57: List[Char] = List(a, a, a, a, a)
scala> List.fill(3)("hello")
res58: List[String] = List(hello, hello, hello)
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If fill is given more than two arguments, then it will make multidimensional lists. That is, it will make lists of lists, lists of lists of lists,
etc. The additional arguments go in the first argument list.
scala> List.fill(2, 3)('b')
res59: List[List[Char]] = List(List(b, b, b), List(b, b, b))
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Tabulating a function: List.tabulate
The tabulate method creates a list whose elements are computed according
to a supplied function. Its arguments are just like those of List.fill: the
first argument list gives the dimensions of the list to create, and the second
describes the elements of the list. The only difference is that instead of the
elements being fixed, they are computed from a function:
scala> val squares = List.tabulate(5)(n => n * n)
squares: List[Int] = List(0, 1, 4, 9, 16)
scala> val multiplication = List.tabulate(5,5)(_ * _)
multiplication: List[List[Int]] = List(List(0, 0, 0, 0, 0),
List(0, 1, 2, 3, 4), List(0, 2, 4, 6, 8),
List(0, 3, 6, 9, 12), List(0, 4, 8, 12, 16))
Concatenating multiple lists: List.concat
The concat method concatenates a number of element lists. The lists to be
concatenated are supplied as direct arguments to concat:
scala> List.concat(List('a', 'b'), List('c'))
res60: List[Char] = List(a, b, c)
scala> List.concat(List(), List('b'), List('c'))
res61: List[Char] = List(b, c)
scala> List.concat()
res62: List[Nothing] = List()
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16.9
Processing multiple lists together
The zipped method on tuples generalizes several common operations to
work on multiple lists instead of just one. One such operation is map. The
map method for two zipped lists maps pairs of elements rather than individual elements. One pair is for the first element of each list, another pair is
for the second element of each list, and so on—as many pairs as the lists are
long. Here is an example of its use:
scala> (List(10, 20), List(3, 4, 5)).zipped.map(_ * _)
res63: List[Int] = List(30, 80)
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Notice that the third element of the second list is discarded. The zipped
method zips up only as many elements as appear in all the lists together.
Any extra elements on the end are discarded.
There are also zipped analogs to exists and forall. They are just like
the single-list versions of those methods except they operate on elements
from multiple lists instead of just one:
scala> (List("abc", "de"), List(3, 2)).zipped.
forall(_.length == _)
res64: Boolean = true
scala> (List("abc", "de"), List(3, 2)).zipped.
exists(_.length != _)
res65: Boolean = false
The fast track
In the next (and final) section of this chapter, we provide insight into
Scala’s type inference algorithm. If you’re not interested in such details
right now, you can skip the entire section and go straight to the conclusion
on page 372.
16.10
Understanding Scala’s type inference algorithm
One difference between the previous uses of sortWith and msort concerns
the admissible syntactic forms of the comparison function.
Compare:
scala> msort((x: Char, y: Char) => x > y)(abcde)
res66: List[Char] = List(e, d, c, b, a)
with:
scala> abcde sortWith (_ > _)
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res67: List[Char] = List(e, d, c, b, a)
The two expressions are equivalent, but the first uses a longer form of comparison function with named parameters and explicit types. The second uses
the concise form, (_ > _), where named parameters are replaced by underscores. Of course, you could also use the first, longer form of comparison
with sortWith.
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However, the short form cannot be used with msort.
scala> msort(_ > _)(abcde)
:12: error: missing parameter type for expanded
function ((x$1, x$2) => x$1.$greater(x$2))
msort(_ > _)(abcde)
ˆ
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To understand why, you need to know some details of Scala’s type inference
algorithm. Type inference in Scala is flow based. In a method application
m(args), the inferencer first checks whether the method m has a known type.
If it does, that type is used to infer the expected type of the arguments. For
instance, in abcde.sortWith(_ > _), the type of abcde is List[Char].
Hence, sortWith is known to be a method that takes an argument of type
(Char, Char) => Boolean and produces a result of type List[Char]. Since
the parameter types of the function arguments are known, they need not be
written explicitly. With what it knows about sortWith, the inferencer can
deduce that (_ > _) should expand to ((x: Char, y: Char) => x > y) where
x and y are some arbitrary fresh names.
Now consider the second case, msort(_ > _)(abcde). The type of
msort is a curried, polymorphic method type that takes an argument of type
(T, T) => Boolean to a function from List[T] to List[T] where T is some
as-yet unknown type. The msort method needs to be instantiated with a type
parameter before it can be applied to its arguments.
Because the precise instance type of msort in the application is not yet
known, it cannot be used to infer the type of its first argument. The type
inferencer changes its strategy in this case; it first type checks method arguments to determine the proper instance type of the method. However, when
tasked to type check the short-hand function literal, (_ > _), it fails because
it has no information about the types of the implicit function parameters that
are indicated by underscores.
One way to resolve the problem is to pass an explicit type parameter to
msort, as in:
scala> msort[Char](_ > _)(abcde)
res68: List[Char] = List(e, d, c, b, a)
Because the correct instance type of msort is now known, it can be used to
infer the type of the arguments. Another possible solution is to rewrite the
msort method so that its parameters are swapped:
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def msortSwapped[T](xs: List[T])(less:
(T, T) => Boolean): List[T] = {
// same implementation as msort,
// but with arguments swapped
}
Now type inference would succeed:
scala> msortSwapped(abcde)(_ > _)
res69: List[Char] = List(e, d, c, b, a)
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What has happened is that the inferencer used the known type of the first
parameter abcde to determine the type parameter of msortSwapped. Once
the precise type of msortSwapped was known, it could be used in turn to
infer the type of the second parameter, (_ > _).
Generally, when tasked to infer the type parameters of a polymorphic
method, the type inferencer consults the types of all value arguments in the
first parameter list but no arguments beyond that. Since msortSwapped is
a curried method with two parameter lists, the second argument (i.e., the
function value) did not need to be consulted to determine the type parameter
of the method.
This inference scheme suggests the following library design principle:
When designing a polymorphic method that takes some non-function arguments and a function argument, place the function argument last in a curried
parameter list on its own. That way, the method’s correct instance type can
be inferred from the non-function arguments, and that type can in turn be
used to type check the function argument. The net effect is that users of the
method will be able to give less type information and write function literals
in more compact ways.
Now to the more complicated case of a fold operation. Why is there
the need for an explicit type parameter in an expression like the body of the
flattenRight method shown on page 363?
(xss :\ List[T]()) (_ ::: _)
The type of the fold-right operation is polymorphic in two type variables.
Given an expression:
(xs :\ z) (op)
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The type of xs must be a list of some arbitrary type A, say xs: List[A]. The
start value z can be of some other type B. The operation op must then take two
arguments of type A and B, and return a result of type B, i.e., op: (A, B) => B.
Because the type of z is not related to the type of the list xs, type inference
has no context information for z.
Now consider the expression in the erroneous version of flattenRight,
also shown on page 363:
(xss :\ List()) (_ ::: _)
// this won't compile
The start value z in this fold is an empty list, List(), so without additional
type information its type is inferred to be a List[Nothing]. Hence, the
inferencer will infer that the B type of the fold is List[Nothing]. Therefore,
the operation (_ ::: _) of the fold is expected to be of the following type:
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(List[T], List[Nothing]) => List[Nothing]
This is indeed a possible type for the operation in that fold but it is not a very
useful one! It says that the operation always takes an empty list as second
argument and always produces an empty list as result.
In other words, the type inference settled too early on a type for List();
it should have waited until it had seen the type of the operation op. So the
(otherwise very useful) rule to only consider the first argument section in a
curried method application for determining the method’s type is at the root
of the problem here. On the other hand, even if that rule were relaxed, the
inferencer still could not come up with a type for op because its parameter
types are not given. Hence, there is a Catch-22 situation that can only be
resolved by an explicit type annotation from the programmer.
This example highlights some limitations of the local, flow-based type
inference scheme of Scala. It is not present in the more global HindleyMilner style of type inference used in functional languages, such as ML or
Haskell. However, Scala’s local type inference deals much more gracefully
with object-oriented subtyping than the Hindley-Milner style does. Fortunately, the limitations show up only in some corner cases, and are usually
easily fixed by adding an explicit type annotation.
Adding type annotations is also a useful debugging technique when you
get confused by type error messages related to polymorphic methods. If you
are unsure what caused a particular type error, just add some type arguments
or other type annotations, which you think are correct. Then you should be
able to quickly see where the real problem is.
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16.11
Chapter 16 · Working with Lists
Conclusion
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Now you have seen many ways to work with lists. You have seen the basic
operations like head and tail, the first-order operations like reverse, the
higher-order operations like map, and the utility methods in the List object.
Along the way, you learned a bit about how Scala’s type inference works.
Lists are a real work horse in Scala, so you will benefit from knowing
how to use them. For that reason, this chapter has delved deeply into how to
use lists. Lists are just one kind of collection that Scala supports, however.
The next chapter is broad, rather than deep, and shows you how to use a
variety of Scala’s collection types.
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Working with Other Collections
Scala has a rich collection library. This chapter gives you a tour of the most
commonly used collection types and operations, showing just the parts you
will use most frequently. Chapter 24 will provide a more comprehensive
tour of what’s available, and Chapter 25 will show how Scala’s composition
constructs are used to provide such a rich API.
17.1
Sequences
Sequence types let you work with groups of data lined up in order. Because
the elements are ordered, you can ask for the first element, second element,
103rd element, and so on. In this section, we’ll give you a quick tour of the
most important sequences.
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Lists
Perhaps the most important sequence type to know about is class List, the
immutable linked-list described in detail in the previous chapter. Lists support fast addition and removal of items to the beginning of the list, but they
do not provide fast access to arbitrary indexes because the implementation
must iterate through the list linearly.
This combination of features might sound odd, but they hit a sweet spot
that works well for many algorithms. The fast addition and removal of initial
elements means that pattern matching works well, as described in Chapter 15. The immutability of lists helps you develop correct, efficient algorithms because you never need to make copies of a list.
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Chapter 17 · Working with Other Collections
Here’s a short example showing how to initialize a list, and access its
head and tail:
scala> val colors = List("red", "blue", "green")
colors: List[String] = List(red, blue, green)
scala> colors.head
res0: String = red
scala> colors.tail
res1: List[String] = List(blue, green)
For a refresher on lists, see Step 8 in Chapter 3. You can find details on
using lists in Chapter 16. Lists will also be discussed in Chapter 22, which
provides insight into how lists are implemented in Scala.
Arrays
Arrays allow you to hold a sequence of elements and efficiently access an
element at an arbitrary position, either to get or update the element, with a
zero-based index. Here’s how you create an array whose size you know, but
for which you don’t yet know the element values:
scala> val fiveInts = new Array[Int](5)
fiveInts: Array[Int] = Array(0, 0, 0, 0, 0)
Here’s how you initialize an array when you do know the element values:
scala> val fiveToOne = Array(5, 4, 3, 2, 1)
fiveToOne: Array[Int] = Array(5, 4, 3, 2, 1)
As mentioned previously, arrays are accessed in Scala by placing an index
in parentheses, not square brackets as in Java. Here’s an example of both
accessing and updating an array element:
scala> fiveInts(0) = fiveToOne(4)
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scala> fiveInts
res3: Array[Int] = Array(1, 0, 0, 0, 0)
Scala arrays are represented in the same way as Java arrays. So, you can
seamlessly use existing Java methods that return arrays.1
1 The
difference in variance of Scala’s and Java’s arrays—i.e., whether Array[String]
is a subtype of Array[AnyRef]—will be discussed in Section 19.3.
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You have seen arrays in action many times in previous chapters. The
basics are in Step 7 in Chapter 3. Several examples of iterating through the
elements of an array with a for expression are shown in Section 7.3. Arrays
also figure prominently in the two-dimensional layout library of Chapter 10.
List buffers
Class List provides fast access to the head of the list, but not the end. Thus,
when you need to build a list by appending to the end, consider building the
list backwards by prepending elements to the front. Then when you’re done,
call reverse to get the elements in the order you need.
Another alternative, which avoids the reverse operation, is to use a
ListBuffer. A ListBuffer is a mutable object (contained in package
scala.collection.mutable), which can help you build lists more efficiently when you need to append. ListBuffer provides constant time append and prepend operations. You append elements with the += operator, and
prepend them with the +=: operator. When you’re done building, you can
obtain a List by invoking toList on the ListBuffer. Here’s an example:
scala> import scala.collection.mutable.ListBuffer
import scala.collection.mutable.ListBuffer
scala> val buf = new ListBuffer[Int]
buf: scala.collection.mutable.ListBuffer[Int] = ListBuffer()
scala> buf += 1
res4: buf.type = ListBuffer(1)
scala> buf += 2
res5: buf.type = ListBuffer(1, 2)
scala> buf
res6: scala.collection.mutable.ListBuffer[Int] =
ListBuffer(1, 2)
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scala> 3 +=: buf
res7: buf.type = ListBuffer(3, 1, 2)
scala> buf.toList
res8: List[Int] = List(3, 1, 2)
Another reason to use ListBuffer instead of List is to prevent the potential for stack overflow. If you can build a list in the desired order by
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prepending, but the recursive algorithm that would be required is not tail
recursive, you can use a for expression or while loop and a ListBuffer
instead. You’ll see ListBuffer being used in this way in Section 22.2.
Array buffers
An ArrayBuffer is like an array, except that you can additionally add and
remove elements from the beginning and end of the sequence. All Array
operations are available, though they are a little slower due to a layer of
wrapping in the implementation. The new addition and removal operations
are constant time on average, but occasionally require linear time due to the
implementation needing to allocate a new array to hold the buffer’s contents.
To use an ArrayBuffer, you must first import it from the mutable collections package:
scala> import scala.collection.mutable.ArrayBuffer
import scala.collection.mutable.ArrayBuffer
When you create an ArrayBuffer, you must specify a type parameter, but
you don’t need to specify a length. The ArrayBuffer will adjust the allocated space automatically as needed:
scala> val buf = new ArrayBuffer[Int]()
buf: scala.collection.mutable.ArrayBuffer[Int] =
ArrayBuffer()
You can append to an ArrayBuffer using the += method:
scala> buf += 12
res9: buf.type = ArrayBuffer(12)
scala> buf += 15
res10: buf.type = ArrayBuffer(12, 15)
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scala> buf
res11: scala.collection.mutable.ArrayBuffer[Int] =
ArrayBuffer(12, 15)
All the normal array methods are available. For example, you can ask an
ArrayBuffer its length or you can retrieve an element by its index:
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scala> buf.length
res12: Int = 2
scala> buf(0)
res13: Int = 12
Strings (via StringOps)
One other sequence to be aware of is StringOps, which implements many
sequence methods. Because Predef has an implicit conversion from String
to StringOps, you can treat any string like a sequence. Here’s an example:
scala> def hasUpperCase(s: String) = s.exists(_.isUpper)
hasUpperCase: (s: String)Boolean
scala> hasUpperCase("Robert Frost")
res14: Boolean = true
scala> hasUpperCase("e e cummings")
res15: Boolean = false
In this example, the exists method is invoked on the string named s in
the hasUpperCase method body. Because no method named “exists” is
declared in class String itself, the Scala compiler will implicitly convert s
to StringOps, which has the method. The exists method treats the string
as a sequence of characters, and will return true if any of the characters are
upper case.2
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17.2
Sets and maps
You have already seen the basics of sets and maps in previous chapters, starting with Step 10 in Chapter 3. In this section, we’ll offer more insight into
their use and show you a few more examples.
As mentioned previously, the Scala collections library offers both mutable and immutable versions of sets and maps. The hierarchy for sets is shown
in Figure 3.2 on page 88, and the hierarchy for maps is shown in Figure 3.3
on page 90. As these diagrams show, the simple names Set and Map are used
by three traits each, residing in different packages.
2 The
code given on page 57 of Chapter 1 presents a similar example.
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By default when you write “Set” or “Map” you get an immutable object.
If you want the mutable variant, you need to do an explicit import. Scala
gives you easier access to the immutable variants, as a gentle encouragement
to prefer them over their mutable counterparts. The easy access is provided
via the Predef object, which is implicitly imported into every Scala source
file. Listing 17.1 shows the relevant definitions:
object Predef {
type Map[A, +B] = collection.immutable.Map[A, B]
type Set[A] = collection.immutable.Set[A]
val Map = collection.immutable.Map
val Set = collection.immutable.Set
// ...
}
Listing 17.1 · Default map and set definitions in Predef.
The “type” keyword is used in Predef to define Set and Map as aliases
for the longer fully qualified names of the immutable set and map traits.3
The vals named Set and Map are initialized to refer to the singleton objects
for the immutable Set and Map. So Map is the same as Predef.Map, which is
defined to be the same as scala.collection.immutable.Map. This holds
both for the Map type and Map object.
If you want to use both mutable and immutable sets or maps in the same
source file, one approach is to import the name of the package that contains
the mutable variants:
scala> import scala.collection.mutable
import scala.collection.mutable
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You can continue to refer to the immutable set as Set, as before, but can now
refer to the mutable set as mutable.Set. Here’s an example:
scala> val mutaSet = mutable.Set(1, 2, 3)
mutaSet: scala.collection.mutable.Set[Int] = Set(1, 2, 3)
3 The type
keyword will be explained in more detail in Section 20.6.
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Using sets
The key characteristic of sets is that they will ensure that at most one of each
object, as determined by ==, will be contained in the set at any one time. As
an example, we’ll use a set to count the number of different words in a string.
The split method on String can separate a string into words, if you
specify spaces and punctuation as word separators. The regular expression
“[ !,.]+” will suffice: It indicates the string should be split at each place
that one or more space and/or punctuation characters exist.
scala> val text = "See Spot run. Run, Spot. Run!"
text: String = See Spot run. Run, Spot. Run!
scala> val wordsArray = text.split("[ !,.]+")
wordsArray: Array[String]
= Array(See, Spot, run, Run, Spot, Run)
To count the distinct words, you can convert them to the same case and then
add them to a set. Because sets exclude duplicates, each distinct word will
appear exactly one time in the set.
First, you can create an empty set using the empty method provided on
the Set companion objects:
scala>
val words = mutable.Set.empty[String]
words: scala.collection.mutable.Set[String] = Set()
Then, just iterate through the words with a for expression, convert each
word to lower case, and add it to the mutable set with the += operator:
scala> for (word <- wordsArray)
words += word.toLowerCase
scala> words
res17: scala.collection.mutable.Set[String] =
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Set(see, run, spot)
Thus, the text contained exactly three distinct words: spot, run, and see.
The most commonly used methods on both mutable and immutable sets are
shown in Table 17.1.
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Table 17.1 · Common operations for sets
What it is
val nums = Set(1, 2, 3)
Creates an immutable set
(nums.toString returns Set(1, 2, 3))
nums + 5
Adds an element (returns
Set(1, 2, 3, 5))
nums - 3
Removes an element (returns Set(1, 2))
nums ++ List(5, 6)
Adds multiple elements (returns
Set(1, 2, 3, 5, 6))
nums -- List(1, 2)
Removes multiple elements (returns
Set(3))
nums & Set(1, 3, 5, 7)
Takes the intersection of two sets (returns
Set(1, 3))
nums.size
Returns the size of the set (returns 3)
nums.contains(3)
Checks for inclusion (returns true)
import scala.collection.mutable
val words =
mutable.Set.empty[String]
Makes the mutable collections easy to
access
Creates an empty, mutable set
(words.toString returns Set())
words += "the"
Adds an element (words.toString
returns Set(the))
words -= "the"
Removes an element, if it exists
(words.toString returns Set())
words ++= List("do", "re", "mi")
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What it does
words --= List("do", "re")
words.clear
Adds multiple elements
(words.toString returns
Set(do, re, mi))
Removes multiple elements
(words.toString returns Set(mi))
Removes all elements (words.toString
returns Set())
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Using maps
Maps let you associate a value with each element of a set. Using a map looks
similar to using an array, except instead of indexing with integers counting
from 0, you can use any kind of key. If you import the mutable package
name, you can create an empty mutable map like this:
scala> val map = mutable.Map.empty[String, Int]
map: scala.collection.mutable.Map[String,Int] = Map()
Note that when you create a map, you must specify two types. The first
type is for the keys of the map, the second for the values. In this case, the
keys are strings and the values are integers. Setting entries in a map looks
similar to setting entries in an array:
scala> map("hello") = 1
scala> map("there") = 2
scala> map
res20: scala.collection.mutable.Map[String,Int] =
Map(hello -> 1, there -> 2)
Likewise, reading a map is similar to reading an array:
scala> map("hello")
res21: Int = 1
Putting it all together, here is a method that counts the number of times
each word occurs in a string:
scala> def countWords(text: String) = {
val counts = mutable.Map.empty[String, Int]
for (rawWord <- text.split("[ ,!.]+")) {
val word = rawWord.toLowerCase
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val oldCount =
if (counts.contains(word)) counts(word)
else 0
counts += (word -> (oldCount + 1))
}
counts
}
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countWords: (text:
String)scala.collection.mutable.Map[String,Int]
scala> countWords("See Spot run! Run, Spot. Run!")
res22: scala.collection.mutable.Map[String,Int] =
Map(spot -> 2, see -> 1, run -> 3)
Given these counts, you can see that this text talks a lot about running, but
not so much about seeing.
The way this code works is that a mutable map, named counts, maps
each word to the number of times it occurs in the text. For each word in the
text, the word’s old count is looked up, that count is incremented by one, and
the new count is saved back into counts. Note the use of contains to check
whether a word has been seen yet or not. If counts.contains(word) is not
true, then the word has not yet been seen and zero is used for the count.
Many of the most commonly used methods on both mutable and immutable maps are shown in Table 17.2.
Table 17.2 · Common operations for maps
What it is
val nums = Map("i" -> 1, "ii" -> 2)
nums + ("vi" -> 6)
nums - "ii"
nums ++ List("iii" -> 3, "v" -> 5)
What it does
Creates an immutable map
(nums.toString returns
Map(i -> 1, ii -> 2))
Adds an entry (returns Map(i -> 1,
ii -> 2, vi -> 6))
Removes an entry (returns Map(i -> 1))
Adds multiple entries (returns
Map(i -> 1, ii -> 2, iii -> 3, v -> 5))
nums -- List("i", "ii")
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nums.size
Removes multiple entries (returns Map())
Returns the size of the map (returns 2)
nums.contains("ii")
Checks for inclusion (returns true)
nums("ii")
Retrieves the value at a specified key
(returns 2)
nums.keys
Returns the keys (returns an Iterable
over the strings "i" and "ii")
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Table 17.2 · continued
nums.keySet
Returns the keys as a set (returns
Set(i, ii))
nums.values
Returns the values (returns an Iterable
over the integers 1 and 2)
nums.isEmpty
Indicates whether the map is empty
(returns false)
import scala.collection.mutable
Makes the mutable collections easy to
access
val words =
mutable.Map.empty[String, Int]
Creates an empty, mutable map
words += ("one" -> 1)
Adds a map entry from "one" to 1
(words.toString returns
Map(one -> 1))
words -= "one"
Removes a map entry, if it exists
(words.toString returns Map())
words ++= List("one" -> 1,
"two" -> 2, "three" -> 3)
Adds multiple map entries
(words.toString returns
Map(one -> 1, two -> 2, three -> 3))
words --= List("one", "two")
Removes multiple objects
(words.toString returns
Map(three -> 3))
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Default sets and maps
For most uses, the implementations of mutable and immutable sets and maps
provided by the Set(), scala.collection.mutable.Map(), etc., factories
will likely be sufficient. The implementations provided by these factories use
a fast lookup algorithm, usually involving a hash table, so they can quickly
decide whether or not an object is in the collection.
The scala.collection.mutable.Set() factory method, for example,
returns a scala.collection.mutable.HashSet, which uses a hash table
internally. Similarly, the scala.collection.mutable.Map() factory returns a scala.collection.mutable.HashMap.
The story for immutable sets and maps is a bit more involved. The class
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Table 17.3 · Default immutable set implementations
Number of elements
0
1
2
3
4
5 or more
Implementation
scala.collection.immutable.EmptySet
scala.collection.immutable.Set1
scala.collection.immutable.Set2
scala.collection.immutable.Set3
scala.collection.immutable.Set4
scala.collection.immutable.HashSet
returned by the scala.collection.immutable.Set() factory method, for
example, depends on how many elements you pass to it, as shown in Table 17.3. For sets with fewer than five elements, a special class devoted
exclusively to sets of each particular size is used to maximize performance.
Once you request a set that has five or more elements in it, however, the
factory method will return an implementation that uses hash tries.
Similarly, the scala.collection.immutable.Map() factory method
will return a different class depending on how many key-value pairs you
pass to it, as shown in Table 17.4. As with sets, for immutable maps with
fewer than five elements, a special class devoted exclusively to maps of each
particular size is used to maximize performance. Once a map has five or
more key-value pairs in it, however, an immutable HashMap is used.
The default immutable implementation classes shown in Tables 17.3
and 17.4 work together to give you maximum performance. For example,
if you add an element to an EmptySet, it will return a Set1. If you add an
element to that Set1, it will return a Set2. If you then remove an element
from the Set2, you’ll get another Set1.
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Table 17.4 · Default immutable map implementations
Number of elements
0
1
2
3
4
5 or more
Implementation
scala.collection.immutable.EmptyMap
scala.collection.immutable.Map1
scala.collection.immutable.Map2
scala.collection.immutable.Map3
scala.collection.immutable.Map4
scala.collection.immutable.HashMap
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Sorted sets and maps
On occasion you may need a set or map whose iterator returns elements in
a particular order. For this purpose, the Scala collections library provides
traits SortedSet and SortedMap. These traits are implemented by classes
TreeSet and TreeMap, which use a red-black tree to keep elements (in the
case of TreeSet) or keys (in the case of TreeMap) in order. The order is
determined by the Ordered trait, which the element type of the set, or key
type of the map, must either mix in or be implicitly convertible to. These
classes only come in immutable variants. Here are some TreeSet examples:
scala> import scala.collection.immutable.TreeSet
import scala.collection.immutable.TreeSet
scala> val ts = TreeSet(9, 3, 1, 8, 0, 2, 7, 4, 6, 5)
ts: scala.collection.immutable.TreeSet[Int] =
TreeSet(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
scala> val cs = TreeSet('f', 'u', 'n')
cs: scala.collection.immutable.TreeSet[Char] =
TreeSet(f, n, u)
And here are a few TreeMap examples:
scala> import scala.collection.immutable.TreeMap
import scala.collection.immutable.TreeMap
scala> var tm = TreeMap(3 -> 'x', 1 -> 'x', 4 -> 'x')
tm: scala.collection.immutable.TreeMap[Int,Char] =
Map(1 -> x, 3 -> x, 4 -> x)
scala> tm += (2 -> 'x')
scala> tm
res30: scala.collection.immutable.TreeMap[Int,Char] =
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Map(1 -> x, 2 -> x, 3 -> x, 4 -> x)
17.3
Selecting mutable versus immutable collections
For some problems, mutable collections work better, while for others, immutable collections work better. When in doubt, it is better to start with an
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immutable collection and change it later, if you need to, because immutable
collections can be easier to reason about than mutable ones.
Also, it can be worthwhile to go the opposite way sometimes. If you find
some code that uses mutable collections becoming complicated and hard to
reason about, consider whether it would help to change some of the collections to immutable alternatives. In particular, if you find yourself worrying
about making copies of mutable collections in just the right places, or thinking a lot about who “owns” or “contains” a mutable collection, consider
switching some of the collections to their immutable counterparts.
Besides being potentially easier to reason about, immutable collections
can usually be stored more compactly than mutable ones if the number of elements stored in the collection is small. For instance an empty mutable map
in its default representation of HashMap takes up about 80 bytes, and about
16 more are added for each entry that’s added to it. An empty immutable
Map is a single object that’s shared between all references, so referring to it
essentially costs just a single pointer field.
What’s more, the Scala collections library currently stores immutable
maps and sets with up to four entries in a single object, which typically
takes up between 16 and 40 bytes, depending on the number of entries stored
in the collection.4 So for small maps and sets, the immutable versions are
much more compact than the mutable ones. Given that many collections are
small, switching them to be immutable can bring important space savings
and performance advantages.
To make it easier to switch from immutable to mutable collections, and
vice versa, Scala provides some syntactic sugar. Even though immutable
sets and maps do not support a true += method, Scala gives a useful alternate
interpretation to +=. Whenever you write a += b, and a does not support a
method named +=, Scala will try interpreting it as a = a + b.
For example, immutable sets do not support a += operator:
scala> val people = Set("Nancy", "Jane")
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people: scala.collection.immutable.Set[String] =
Set(Nancy, Jane)
scala> people += "Bob"
:14: error: value += is not a member of
4 The “single object” is an instance of Set1 through Set4, or Map1 through Map4, as
shown in Tables 17.3 and 17.4.
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scala.collection.immutable.Set[String]
people += "Bob"
ˆ
However, if you declare people as a var, instead of a val, then the collection
can be “updated” with a += operation, even though it is immutable. First, a
new collection will be created, and then people will be reassigned to refer
to the new collection:
scala> var people = Set("Nancy", "Jane")
people: scala.collection.immutable.Set[String] =
Set(Nancy, Jane)
scala> people += "Bob"
scala> people
res34: scala.collection.immutable.Set[String] =
Set(Nancy, Jane, Bob)
After this series of statements, the people variable refers to a new immutable
set, which contains the added string, "Bob". The same idea applies to any
method ending in =, not just the += method. Here’s the same syntax used
with the -= operator, which removes an element from a set, and the ++=
operator, which adds a collection of elements to a set:
scala> people -= "Jane"
scala> people ++= List("Tom", "Harry")
scala> people
res37: scala.collection.immutable.Set[String] =
Set(Nancy, Bob, Tom, Harry)
To see how this is useful, consider again the following Map example from
Section 1.1:
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var capital = Map("US" -> "Washington", "France" -> "Paris")
capital += ("Japan" -> "Tokyo")
println(capital("France"))
This code uses immutable collections. If you want to try using mutable collections instead, all that is necessary is to import the mutable version of Map,
thus overriding the default import of the immutable Map:
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import scala.collection.mutable.Map
// only change needed!
var capital = Map("US" -> "Washington", "France" -> "Paris")
capital += ("Japan" -> "Tokyo")
println(capital("France"))
Not all examples are quite that easy to convert, but the special treatment of
methods ending in an equals sign will often reduce the amount of code that
needs changing.
By the way, this syntactic treatment works on any kind of value, not just
collections. For example, here it is being used on floating-point numbers:
scala> var roughlyPi = 3.0
roughlyPi: Double = 3.0
scala> roughlyPi += 0.1
scala> roughlyPi += 0.04
scala> roughlyPi
res40: Double = 3.14
The effect of this expansion is similar to Java’s assignment operators (+=,
-=, *=, etc.), but it is more general because every operator ending in = can
be converted.
17.4
Initializing collections
As you’ve seen previously, the most common way to create and initialize a
collection is to pass the initial elements to a factory method on the companion
object of your chosen collection. You just place the elements in parentheses
after the companion object name, and the Scala compiler will transform that
to an invocation of an apply method on that companion object:
scala> List(1, 2, 3)
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res41: List[Int] = List(1, 2, 3)
scala> Set('a', 'b', 'c')
res42: scala.collection.immutable.Set[Char] = Set(a, b, c)
scala> import scala.collection.mutable
import scala.collection.mutable
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scala> mutable.Map("hi" -> 2, "there" -> 5)
res43: scala.collection.mutable.Map[String,Int] =
Map(hi -> 2, there -> 5)
scala> Array(1.0, 2.0, 3.0)
res44: Array[Double] = Array(1.0, 2.0, 3.0)
Although most often you can let the Scala compiler infer the element
type of a collection from the elements passed to its factory method, sometimes you may want to create a collection but specify a different type from
the one the compiler would choose. This is especially an issue with mutable
collections. Here’s an example:
scala> import scala.collection.mutable
import scala.collection.mutable
scala> val stuff = mutable.Set(42)
stuff: scala.collection.mutable.Set[Int] = Set(42)
scala> stuff += "abracadabra"
:16: error: type mismatch;
found
: String("abracadabra")
required: Int
stuff += "abracadabra"
ˆ
The problem here is that stuff was given an element type of Int. If you
want it to have an element type of Any, you need to say so explicitly by
putting the element type in square brackets, like this:
scala> val stuff = mutable.Set[Any](42)
stuff: scala.collection.mutable.Set[Any] = Set(42)
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Another special situation is if you want to initialize a collection with
another collection. For example, imagine you have a list, but you want a
TreeSet containing the elements in the list. Here’s the list:
scala> val colors = List("blue", "yellow", "red", "green")
colors: List[String] = List(blue, yellow, red, green)
You cannot pass the colors list to the factory method for TreeSet:
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scala> import scala.collection.immutable.TreeSet
import scala.collection.immutable.TreeSet
scala> val treeSet = TreeSet(colors)
:16: error: No implicit Ordering defined for
List[String].
val treeSet = TreeSet(colors)
ˆ
Instead, you’ll need to create an empty TreeSet[String] and add to it the
elements of the list with the TreeSet’s ++ operator:
scala> val treeSet = TreeSet[String]() ++ colors
treeSet: scala.collection.immutable.TreeSet[String] =
TreeSet(blue, green, red, yellow)
Converting to array or list
If you need to initialize a list or array with another collection, on the other
hand, it is quite straightforward. As you’ve seen previously, to initialize a
new list with another collection, simply invoke toList on that collection:
scala> treeSet.toList
res50: List[String] = List(blue, green, red, yellow)
Or, if you need an array, invoke toArray:
scala> treeSet.toArray
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res51: Array[String] = Array(blue, green, red, yellow)
Note that although the original colors list was not sorted, the elements in
the list produced by invoking toList on the TreeSet are in alphabetical
order. When you invoke toList or toArray on a collection, the order of the
elements in the resulting list or array will be the same as the order of elements
produced by an iterator obtained by invoking elements on that collection.
Because a TreeSet[String]’s iterator will produce strings in alphabetical
order, those strings will appear in alphabetical order in the list resulting from
invoking toList on that TreeSet.
Keep in mind, however, that conversion to lists or arrays usually requires
copying all of the elements of the collection, and thus may be slow for large
collections. Sometimes you need to do it, though, due to an existing API.
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Further, many collections only have a few elements anyway, in which case
there is only a small speed penalty.
Converting between mutable and immutable sets and maps
Another situation that arises occasionally is the need to convert a mutable
set or map to an immutable one, or vice versa. To accomplish this, you can
use the technique shown on the previous page to initialize a TreeSet with
the elements of a list. Create a collection of the new type using the empty
method and then add the new elements using either ++ or ++=, whichever
is appropriate for the target collection type. Here’s how you’d convert the
immutable TreeSet from the previous example to a mutable set, and back
again to an immutable one:
scala> import scala.collection.mutable
import scala.collection.mutable
scala> treeSet
res52: scala.collection.immutable.TreeSet[String] =
TreeSet(blue, green, red, yellow)
scala> val mutaSet = mutable.Set.empty ++= treeSet
mutaSet: scala.collection.mutable.Set[String] =
Set(red, blue, green, yellow)
scala> val immutaSet = Set.empty ++ mutaSet
immutaSet: scala.collection.immutable.Set[String] =
Set(red, blue, green, yellow)
You can use the same technique to convert between mutable and immutable maps:
scala> val muta = mutable.Map("i" -> 1, "ii" -> 2)
muta: scala.collection.mutable.Map[String,Int] =
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Map(ii -> 2,i -> 1)
scala> val immu = Map.empty ++ muta
immu: scala.collection.immutable.Map[String,Int] =
Map(ii -> 2, i -> 1)
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17.5
Tuples
As described in Step 9 in Chapter 3, a tuple combines a fixed number of
items together so that they can be passed around as a whole. Unlike an array
or list, a tuple can hold objects with different types. Here is an example of a
tuple holding an integer, a string, and the console:
(1, "hello", Console)
Tuples save you the tedium of defining simplistic data-heavy classes. Even
though defining a class is already easy, it does require a certain minimum
effort, which sometimes serves no purpose. Tuples save you the effort of
choosing a name for the class, choosing a scope to define the class in, and
choosing names for the members of the class. If your class simply holds
an integer and a string, there is no clarity added by defining a class named
AnIntegerAndAString.
Because tuples can combine objects of different types, tuples do not inherit from Traversable. If you find yourself wanting to group exactly one
integer and exactly one string, then you want a tuple, not a List or Array.
A common application of tuples is returning multiple values from a
method. For example, here is a method that finds the longest word in a
collection and also returns its index:
def longestWord(words: Array[String]) = {
var word = words(0)
var idx = 0
for (i <- 1 until words.length)
if (words(i).length > word.length) {
word = words(i)
idx = i
}
(word, idx)
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}
Here is an example use of the method:
scala> val longest =
longestWord("The quick brown fox".split(" "))
longest: (String, Int) = (quick,1)
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The longestWord function here computes two items: word, the longest
word in the array, and idx, the index of that word. To keep things simple, the function assumes there is at least one word in the list, and it breaks
ties by choosing the word that comes earlier in the list. Once the function
has chosen which word and index to return, it returns both of them together
using the tuple syntax (word, idx).
To access elements of a tuple, you can use method _1 to access the first
element, _2 to access the second, and so on:
scala> longest._1
res53: String = quick
scala> longest._2
res54: Int = 1
Additionally, you can assign each element of the tuple to its own variable,5 like this:
scala> val (word, idx) = longest
word: String = quick
idx: Int = 1
scala> word
res55: String = quick
By the way, if you leave off the parentheses you get a different result:
scala> val word, idx = longest
word: (String, Int) = (quick,1)
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idx: (String, Int) = (quick,1)
This syntax gives multiple definitions of the same expression. Each variable
is initialized with its own evaluation of the expression on the right-hand side.
That the expression evaluates to a tuple in this case does not matter. Both
variables are initialized to the tuple in its entirety. See Chapter 18 for some
examples where multiple definitions are convenient.
As a note of warning, tuples are almost too easy to use. Tuples are great
when you combine data that has no meaning beyond “an A and a B.” However, whenever the combination has some meaning, or you want to add some
5 This
syntax is actually a special case of pattern matching, as described in detail in
Section 15.7.
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methods to the combination, it is better to go ahead and create a class. For
example, do not use a 3-tuple for the combination of a month, a day, and
a year. Make a Date class. It makes your intentions explicit, which both
clears up the code for human readers and gives the compiler and language
opportunities to help you catch mistakes.
17.6
Conclusion
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This chapter has given an overview of the Scala collections library and the
most important classes and traits in it. With this foundation you should be
able to work effectively with Scala collections, and know where to look in
Scaladoc when you need more information. For more detailed information
about Scala collections, look ahead to Chapter 24 and Chapter 25. For now,
in the next chapter, we’ll turn our attention from the Scala library back to the
language and discuss Scala’s support for mutable objects.
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394
Chapter 18
Mutable Objects
In previous chapters, we put the spotlight on functional (immutable) objects.
We did so because the idea of objects without any mutable state deserves to
be better known. However, it is also perfectly possible to define objects with
mutable state in Scala. Such mutable objects often come up naturally when
you want to model objects in the real world that change over time.
This chapter explains what mutable objects are and what Scala provides
in terms of syntax to express them. We will also introduce a larger case
study on discrete event simulation, which involves mutable objects, as well
as building an internal DSL for defining digital circuits to simulate.
18.1
What makes an object mutable?
You can observe the principal difference between a purely functional object and a mutable one even without looking at the object’s implementation.
When you invoke a method or dereference a field on some purely functional
object, you will always get the same result.
For instance, given a list of characters:
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val cs = List('a', 'b', 'c')
an application of cs.head will always return 'a'. This is the case even if
there is an arbitrary number of operations on the list cs between the point
where it is defined and the point where the access cs.head is made.
For a mutable object, on the other hand, the result of a method call or field
access may depend on what operations were previously performed on the
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Section 18.1
Chapter 18 · Mutable Objects
object. A good example of a mutable object is a bank account. Listing 18.1
shows a simplified implementation of bank accounts:
class BankAccount {
private var bal: Int = 0
def balance: Int = bal
def deposit(amount: Int) = {
require(amount > 0)
bal += amount
}
def withdraw(amount: Int): Boolean =
if (amount > bal) false
else {
bal -= amount
true
}
}
Listing 18.1 · A mutable bank account class.
The BankAccount class defines a private variable, bal, and three public methods: balance returns the current balance; deposit adds a given
amount to bal; and withdraw tries to subtract a given amount from bal
while assuring that the remaining balance won’t be negative. The return
value of withdraw is a Boolean indicating whether the requested funds were
successfully withdrawn.
Even if you know nothing about the inner workings of the BankAccount
class, you can still tell that BankAccounts are mutable objects:
scala> val account = new BankAccount
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account: BankAccount = BankAccount@21cf775d
scala> account deposit 100
scala> account withdraw 80
res1: Boolean = true
scala>
account withdraw 80
res2: Boolean = false
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Section 18.1
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Note that the two final withdrawals in the previous interaction returned different results. The first withdraw operation returned true because the bank
account contained sufficient funds to allow the withdrawal. The second operation, although the same as the first one, returned false because the balance
of the account had been reduced so that it no longer covered the requested
funds. So, clearly, bank accounts have mutable state, because the same operation can return different results at different times.
You might think that the mutability of BankAccount is immediately apparent because it contains a var definition. Mutation and vars usually go
hand in hand, but things are not always so clear cut. For instance, a class
might be mutable without defining or inheriting any vars because it forwards method calls to other objects that have mutable state. The reverse is
also possible: A class might contain vars and still be purely functional. An
example would be a class that caches the result of an expensive operation in
a field for optimization purposes. To pick an example, assume the following
unoptimized class Keyed with an expensive operation computeKey:
class Keyed {
def computeKey: Int = ... // this will take some time
...
}
Provided that computeKey neither reads nor writes any vars, you can make
Keyed more efficient by adding a cache:
class MemoKeyed extends Keyed {
private var keyCache: Option[Int] = None
override def computeKey: Int = {
if (!keyCache.isDefined) keyCache = Some(super.computeKey)
keyCache.get
}
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}
Using MemoKeyed instead of Keyed can speed things up because the second time the result of the computeKey operation is requested, the value
stored in the keyCache field can be returned instead of running computeKey
once again. But except for this speed gain, the behavior of class Keyed and
MemoKeyed is exactly the same. Consequently, if Keyed is purely functional,
then so is MemoKeyed, even though it contains a reassignable variable.
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Section 18.2
18.2
Chapter 18 · Mutable Objects
Reassignable variables and properties
You can perform two fundamental operations on a reassignable variable: get
its value or set it to a new value. In libraries such as JavaBeans, these operations are often encapsulated in separate getter and setter methods, which
need to be defined explicitly.
In Scala, every var that is a non-private member of some object implicitly defines a getter and a setter method with it. These getters and setters are
named differently from the Java convention, however. The getter of a var x
is just named “x”, while its setter is named “x_=”.
For example, if it appears in a class, the var definition:
var hour = 12
generates a getter, “hour”, and setter, “hour_=”, in addition to a reassignable
field. The field is always marked private[this], which means it can be accessed only from the object that contains it. The getter and setter, on the other
hand, get the same visibility as the original var. If the var definition is public, so are its getter and setter. If it is protected, they are also protected,
and so on.
For instance, consider the class Time shown in Listing 18.2, which defines two public vars named hour and minute:
class Time {
var hour = 12
var minute = 0
}
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Listing 18.2 · A class with public vars.
This implementation is exactly equivalent to the class definition shown
in Listing 18.3. In the definitions shown in Listing 18.3, the names of the
local fields h and m are arbitrarily chosen so as not to clash with any names
already in use.
An interesting aspect about this expansion of vars into getters and setters
is that you can also choose to define a getter and a setter directly, instead of
defining a var. By defining these access methods directly you can interpret
the operations of variable access and variable assignment as you like. For inCover · Overview · Contents · Discuss · Suggest · Glossary · Index
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class Time {
private[this] var h = 12
private[this] var m = 0
def hour: Int = h
def hour_=(x: Int) = { h = x }
def minute: Int = m
def minute_=(x: Int) = { m = x }
}
Listing 18.3 · How public vars are expanded into getter and setter methods.
stance, the variant of class Time shown in Listing 18.4 contains requirements
that catch all assignments to hour and minute with illegal values.
class Time {
private[this] var h = 12
private[this] var m = 0
def hour: Int = h
def hour_= (x: Int) = {
require(0 <= x && x < 24)
h = x
}
def minute = m
def minute_= (x: Int) = {
require(0 <= x && x < 60)
m = x
}
}
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Listing 18.4 · Defining getter and setter methods directly.
Some languages have a special syntactic construct for these variablelike quantities that are not plain variables in that their getter or setter can
be redefined. For instance, C# has properties, which fulfill this role. In
effect, Scala’s convention of always interpreting a variable as a pair of setter
and getter methods gives you the same capabilities as C# properties without
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requiring special syntax.
Properties can serve many different purposes. In the example shown in
Listing 18.4, the setters enforced an invariant, thus protecting the variable
from being assigned illegal values. You could also use a property to log all
accesses to getters or setters of a variable. Or you could integrate variables
with events, for instance by notifying some subscriber methods each time a
variable is modified (you’ll see examples of this in Chapter 35).
It’s also possible, and sometimes useful, to define a getter and a setter
without an associated field. For example, Listing 18.5 shows a Thermometer
class, which encapsulates a temperature variable that can be read and updated. Temperatures can be expressed in Celsius or Fahrenheit degrees. This
class allows you to get and set the temperature in either measure.
class Thermometer {
var celsius: Float = _
def fahrenheit = celsius * 9 / 5 + 32
def fahrenheit_= (f: Float) = {
celsius = (f - 32) * 5 / 9
}
override def toString = fahrenheit + "F/" + celsius + "C"
}
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Listing 18.5 · Defining a getter and setter without an associated field.
The first line in the body of this class defines a var, celsius, which
will contain the temperature in degrees Celsius. The celsius variable is
initially set to a default value by specifying ‘_’ as the “initializing value”
of the variable. More precisely, an initializer “= _” of a field assigns a zero
value to that field. The zero value depends on the field’s type. It is 0 for
numeric types, false for booleans, and null for reference types. This is the
same as if the same variable was defined in Java without an initializer.
Note that you cannot simply leave off the “= _” initializer in Scala. If
you had written:
var celsius: Float
this would declare an abstract variable, not an uninitialized one.1
1 Abstract
variables will be explained in Chapter 20.
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The celsius variable definition is followed by a getter, “fahrenheit”,
and a setter, “fahrenheit_=”, which access the same temperature, but in degrees Fahrenheit. There is no separate field that contains the current temperature value in Fahrenheit. Instead the getter and setter methods for Fahrenheit values automatically convert from and to degrees Celsius, respectively.
Here’s an example of interacting with a Thermometer object:
scala> val t = new Thermometer
t: Thermometer = 32.0F/0.0C
scala> t.celsius = 100
t.celsius: Float = 100.0
scala> t
res3: Thermometer = 212.0F/100.0C
scala> t.fahrenheit = -40
t.fahrenheit: Float = -40.0
scala> t
res4: Thermometer = -40.0F/-40.0C
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18.3
Case study: Discrete event simulation
The rest of this chapter shows by way of an extended example how mutable objects can be combined with first-class function values in interesting
ways. You’ll see the design and implementation of a simulator for digital
circuits. This task is broken down into several subproblems, each of which
is interesting individually.
First, you’ll see a little language for digital circuits. The definition of
this language will highlight a general method for embedding domain-specific
languages (DSL) in a host language like Scala. Second, we’ll present a simple but general framework for discrete event simulation. Its main task will be
to keep track of actions that are performed in simulated time. Finally, we’ll
show how discrete simulation programs can be structured and built. The
idea of such simulations is to model physical objects by simulated objects,
and use the simulation framework to model physical time.
The example is taken from the classic textbook Structure and Interpretation of Computer Programs by Abelson and Sussman [Abe96]. What’s different here is that the implementation language is Scala instead of Scheme,
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inverter
and-gate
402
or-gate
Figure 18.1 · Basic gates.
and that the various aspects of the example are structured into four software
layers: one for the simulation framework, another for the basic circuit simulation package, a third for a library of user-defined circuits, and the last layer
for each simulated circuit itself. Each layer is expressed as a class, and more
specific layers inherit from more general ones.
The fast track
Understanding the discrete event simulation example presented in this
chapter will take some time. If you feel you want to get on with learning
more Scala instead, it’s safe to skip ahead to the next chapter.
18.4
A language for digital circuits
We’ll start with a “little language” to describe digital circuits. A digital circuit is built from wires and function boxes. Wires carry signals, which are
transformed by function boxes. Signals are represented by booleans: true
for signal-on and false for signal-off.
Figure 18.1 shows three basic function boxes (or gates):
• An inverter, which negates its signal.
• An and-gate, which sets its output to the conjunction of its inputs.
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• An or-gate, which sets its output to the disjunction of its inputs.
These gates are sufficient to build all other function boxes. Gates have delays, so an output of a gate will change only some time after its inputs change.
We’ll describe the elements of a digital circuit by the following set of
Scala classes and functions. First, there is a class Wire for wires. We can
construct wires like this:
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val a = new Wire
val b = new Wire
val c = new Wire
or, equivalent but shorter, like this:
val a, b, c = new Wire
Second, there are three procedures which “make” the basic gates we need:
def inverter(input: Wire, output: Wire)
def andGate(a1: Wire, a2: Wire, output: Wire)
def orGate(o1: Wire, o2: Wire, output: Wire)
What’s unusual, given the functional emphasis of Scala, is that these procedures construct the gates as a side effect, instead of returning the constructed
gates as a result. For instance, an invocation of inverter(a, b) places an
inverter between the wires a and b. It turns out that this side-effecting construction makes it easier to construct complicated circuits gradually. Also,
although methods most often have verb names, these have noun names that
indicate which gate they are making. This reflects the declarative nature of
the DSL: it should describe a circuit, not the actions of making one.
More complicated function boxes can be built from the basic gates. For
instance, the method shown in Listing 18.6 constructs a half-adder. The
halfAdder method takes two inputs, a and b, and produces a sum, s, defined
by “s = (a + b) % 2” and a carry, c, defined by “c = (a + b) / 2”. A diagram
of the half-adder is shown in Figure 18.2.
def halfAdder(a: Wire, b: Wire, s: Wire, c: Wire) = {
val d, e = new Wire
orGate(a, b, d)
andGate(a, b, c)
inverter(c, e)
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andGate(d, e, s)
}
Listing 18.6 · The halfAdder method.
Note that halfAdder is a parameterized function box just like the three
methods that construct the primitive gates. You can use the halfAdder
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a
404
d
e
s
c
b
Figure 18.2 · A half-adder circuit.
method to construct more complicated circuits. For instance, Listing 18.7
defines a full, one-bit adder, shown in Figure 18.3, which takes two inputs, a and b, as well as a carry-in, cin, and which produces a sum output
defined by “sum = (a + b + cin) % 2” and a carry-out output defined by
“cout = (a + b + cin) / 2”.
def fullAdder(a: Wire, b: Wire, cin: Wire,
sum: Wire, cout: Wire) = {
val s, c1, c2 = new Wire
halfAdder(a, cin, s, c1)
halfAdder(b, s, sum, c2)
orGate(c1, c2, cout)
}
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Listing 18.7 · The fullAdder method.
Class Wire and functions inverter, andGate, and orGate represent
a little language with which users can define digital circuits. It’s a good
example of an internal DSL, a domain-specific language defined as a library
in a host language instead of being implemented on its own.
The implementation of the circuit DSL still needs to be worked out.
Since the purpose of defining a circuit in the DSL is simulating the circuit, it
makes sense to base the DSL implementation on a general API for discrete
event simulation. The next two sections will present first the simulation API
and then the implementation of the circuit DSL on top of it.
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b
a
cin
half
adder
s
half
adder
405
sum
c2
c1
cout
Figure 18.3 · A full-adder circuit.
18.5
The Simulation API
The simulation API is shown in Listing 18.8. It consists of class Simulation
in package org.stairwaybook.simulation. Concrete simulation libraries
inherit this class and augment it with domain-specific functionality. The
elements of the Simulation class are presented in this section.
A discrete event simulation performs user-defined actions at specified
times. The actions, which are defined by concrete simulation subclasses, all
share a common type:
type Action = () => Unit
This statement defines Action to be an alias of the type of procedure that
takes an empty parameter list and returns Unit. Action is a type member
of class Simulation. You can think of it as a more readable name for type
() => Unit. Type members will be described in detail in Section 20.6.
The time at which an action is performed is simulated time; it has nothing
to do with the actual “wall clock” time. Simulated times are represented
simply as integers. The current simulated time is kept in a private variable:
private var curtime: Int = 0
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The variable has a public accessor method, which retrieves the current time:
def currentTime: Int = curtime
This combination of private variable with public accessor is used to make
sure that the current time cannot be modified outside the Simulation class.
After all, you don’t usually want your simulation objects to manipulate the
current time, except possibly if your simulation models time travel.
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abstract class Simulation {
type Action = () => Unit
case class WorkItem(time: Int, action: Action)
private var curtime = 0
def currentTime: Int = curtime
private var agenda: List[WorkItem] = List()
private def insert(ag: List[WorkItem],
item: WorkItem): List[WorkItem] = {
if (ag.isEmpty || item.time < ag.head.time) item :: ag
else ag.head :: insert(ag.tail, item)
}
def afterDelay(delay: Int)(block: => Unit) = {
val item = WorkItem(currentTime + delay, () => block)
agenda = insert(agenda, item)
}
private def next() = {
(agenda: @unchecked) match {
case item :: rest =>
agenda = rest
curtime = item.time
item.action()
}
}
def run() = {
afterDelay(0) {
println("*** simulation started, time = " +
currentTime + " ***")
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}
while (!agenda.isEmpty) next()
}
}
Listing 18.8 · The Simulation class.
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An action that needs to be executed at a specified time is called a work
item. Work items are implemented by the following class:
case class WorkItem(time: Int, action: Action)
We made the WorkItem class a case class because of the syntactic conveniences this entails: You can use the factory method, WorkItem, to create
instances of the class, and you get accessors for the constructor parameters
time and action for free. Note also that class WorkItem is nested inside
class Simulation. Nested classes in Scala are treated similarly to Java. Section 20.7 will give more details.
The Simulation class keeps an agenda of all remaining work items that
have not yet been executed. The work items are sorted by the simulated time
at which they have to be run:
private var agenda: List[WorkItem] = List()
The agenda list will be kept in the proper sorted order by the insert method,
which updates it. You can see insert being called from afterDelay, which
is the only way to add a work item to the agenda:
def afterDelay(delay: Int)(block: => Unit) = {
val item = WorkItem(currentTime + delay, () => block)
agenda = insert(agenda, item)
}
As the name implies, this method inserts an action (given by block) into the
agenda so that it is scheduled for execution delay time units after the current
simulation time. For instance, the following invocation would create a new
work item to be executed at the simulated time, currentTime + delay:
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afterDelay(delay) { count += 1 }
The code to be executed is contained in the method’s second argument. The
formal parameter for this argument has type “=> Unit” (i.e., it is a computation of type Unit which is passed by name). Recall that by-name parameters
are not evaluated when passed to a method. So in the call above, count
would be incremented only when the simulation framework calls the action
stored in the work item. Note that afterDelay is a curried function. It’s a
good example of the principle set forward in Section 9.5 that currying can be
used to make method calls look more like built-in syntax.
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The created work item still needs to be inserted into the agenda. This is
done by the insert method, which maintains the invariant that the agenda
is time-sorted:
private def insert(ag: List[WorkItem],
item: WorkItem): List[WorkItem] = {
if (ag.isEmpty || item.time < ag.head.time) item :: ag
else ag.head :: insert(ag.tail, item)
}
The core of the Simulation class is defined by the run method:
def run() = {
afterDelay(0) {
println("*** simulation started, time = " +
currentTime + " ***")
}
while (!agenda.isEmpty) next()
}
This method repeatedly takes the first item in the agenda, removes it from
the agenda and executes it. It does this until there are no more items left in
the agenda to execute. Each step is performed by calling the next method,
which is defined as follows:
private def next() = {
(agenda: @unchecked) match {
case item :: rest =>
agenda = rest
curtime = item.time
item.action()
}
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}
The next method decomposes the current agenda with a pattern match into
a front item, item, and a remaining list of work items, rest. It removes the
front item from the current agenda, sets the simulated time curtime to the
work item’s time, and executes the work item’s action.
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Note that next can be called only if the agenda is non-empty. There’s no
case for an empty list, so you would get a MatchError exception if you tried
to run next on an empty agenda.
In fact, the Scala compiler would normally warn you that you missed one
of the possible patterns for a list:
Simulator.scala:19: warning: match is not exhaustive!
missing combination
Nil
agenda match {
ˆ
one warning found
In this case, the missing case is not a problem because you know that next
is called only on a non-empty agenda. Therefore, you might want to disable the warning. You saw in Section 15.5 that this can be done by adding
an @unchecked annotation to the selector expression of the pattern match.
That’s why the Simulation code uses “(agenda: @unchecked) match”,
not “agenda match”.
That’s it. This might look like surprisingly little code for a simulation
framework. You might wonder how this framework could possibly support interesting simulations, if all it does is execute a list of work items?
In fact the power of the simulation framework comes from the fact that actions stored in work items can themselves install further work items into the
agenda when they are executed. That makes it possible to have long-running
simulations evolve from simple beginnings.
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18.6
Circuit Simulation
The next step is to use the simulation framework to implement the domainspecific language for circuits shown in Section 18.4. Recall that the circuit DSL consists of a class for wires and methods that create and-gates, orgates, and inverters. These are all contained in a BasicCircuitSimulation
class, which extends the simulation framework. This class is shown in Listings 18.9 and 18.10.
Class BasicCircuitSimulation declares three abstract methods that
represent the delays of the basic gates: InverterDelay, AndGateDelay,
and OrGateDelay. The actual delays are not known at the level of this class
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package org.stairwaybook.simulation
abstract class BasicCircuitSimulation extends Simulation {
def InverterDelay: Int
def AndGateDelay: Int
def OrGateDelay: Int
class Wire {
private var sigVal = false
private var actions: List[Action] = List()
def getSignal = sigVal
def setSignal(s: Boolean) =
if (s != sigVal) {
sigVal = s
actions foreach (_ ())
}
def addAction(a: Action) = {
actions = a :: actions
a()
}
}
def inverter(input: Wire, output: Wire) = {
def invertAction() = {
val inputSig = input.getSignal
afterDelay(InverterDelay) {
output setSignal !inputSig
}
}
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input addAction invertAction
}
// continued in Listing 18.10...
Listing 18.9 · The first half of the BasicCircuitSimulation class.
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// ...continued from Listing 18.9
def andGate(a1: Wire, a2: Wire, output: Wire) = {
def andAction() = {
val a1Sig = a1.getSignal
val a2Sig = a2.getSignal
afterDelay(AndGateDelay) {
output setSignal (a1Sig & a2Sig)
}
}
a1 addAction andAction
a2 addAction andAction
}
def orGate(o1: Wire, o2: Wire, output: Wire) = {
def orAction() = {
val o1Sig = o1.getSignal
val o2Sig = o2.getSignal
afterDelay(OrGateDelay) {
output setSignal (o1Sig | o2Sig)
}
}
o1 addAction orAction
o2 addAction orAction
}
def probe(name: String, wire: Wire) = {
def probeAction() = {
println(name + " " + currentTime +
" new-value = " + wire.getSignal)
}
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wire addAction probeAction
}
}
Listing 18.10 · The second half of the BasicCircuitSimulation class.
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because they depend on the technology of circuits that are simulated. That’s
why the delays are left abstract in class BasicCircuitSimulation, so that
their concrete definition is delegated to a subclass.2 The implementation of
class BasicCircuitSimulation’s other members is described next.
The Wire class
A wire needs to support three basic actions:
getSignal: Boolean: returns the current signal on the wire.
setSignal(sig: Boolean): sets the wire’s signal to sig.
addAction(p: Action): attaches the specified procedure p to the ac-
tions of the wire. The idea is that all action procedures attached to
some wire will be executed every time the signal of the wire changes.
Typically actions are added to a wire by components connected to the
wire. An attached action is executed once at the time it is added to a
wire, and after that, every time the signal of the wire changes.
Here is the implementation of the Wire class:
class Wire {
private var sigVal = false
private var actions: List[Action] = List()
def getSignal = sigVal
def setSignal(s: Boolean) =
if (s != sigVal) {
sigVal = s
actions foreach (_ ())
}
def addAction(a: Action) = {
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actions = a :: actions
a()
}
}
2 The
names of these “delay” methods start with a capital letter because they represent
constants. They are methods so they can be overridden in subclasses. You’ll find out how to
do the same thing with vals in Section 20.3.
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Two private variables make up the state of a wire. The variable sigVal represents the current signal, and the variable actions represents the action
procedures currently attached to the wire. The only interesting method implementation is the one for setSignal: When the signal of a wire changes,
the new value is stored in the variable sigVal. Furthermore, all actions attached to a wire are executed. Note the shorthand syntax for doing this:
“actions foreach (_ ())” applies the function, “_ ()”, to each element in
the actions list. As described in Section 8.5, the function “_ ()” is a shorthand for “f => f ()”—i.e., it takes a function (we’ll call it f) and applies it
to the empty parameter list.
The inverter method
The only effect of creating an inverter is that an action is installed on its
input wire. This action is invoked once at the time the action is installed,
and thereafter every time the signal on the input changes. The effect of the
action is that the value of the inverter’s output value is set (via setSignal)
to the inverse of its input value. Since inverter gates have delays, this change
should take effect only InverterDelay units of simulated time after the
input value has changed and the action was executed. This suggests the
following implementation:
def inverter(input: Wire, output: Wire) = {
def invertAction() = {
val inputSig = input.getSignal
afterDelay(InverterDelay) {
output setSignal !inputSig
}
}
input addAction invertAction
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}
The effect of the inverter method is to add invertAction to the input
wire. This action, when invoked, gets the input signal and installs another
action that inverts the output signal into the simulation agenda. This other
action is to be executed after InverterDelay units of simulated time. Note
how the method uses the afterDelay method of the simulation framework
to create a new work item that’s going to be executed in the future.
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The andGate and orGate methods
The implementation of and-gates is analogous to the implementation of inverters. The purpose of an and-gate is to output the conjunction of its input
signals. This should happen at AndGateDelay simulated time units after any
one of its two inputs changes. Hence, the following implementation:
def andGate(a1: Wire, a2: Wire, output: Wire) = {
def andAction() = {
val a1Sig = a1.getSignal
val a2Sig = a2.getSignal
afterDelay(AndGateDelay) {
output setSignal (a1Sig & a2Sig)
}
}
a1 addAction andAction
a2 addAction andAction
}
The effect of the andGate method is to add andAction to both of its input
wires a1 and a2. This action, when invoked, gets both input signals and
installs another action that sets the output signal to the conjunction of both
input signals. This other action is to be executed after AndGateDelay units
of simulated time. Note that the output has to be recomputed if either of the
input wires changes. That’s why the same andAction is installed on each of
the two input wires a1 and a2. The orGate method is implemented similarly,
except it performs a logical-or instead of a logical-and.
Simulation output
To run the simulator, you need a way to inspect changes of signals on wires.
To accomplish this, you can simulate the action of putting a probe on a wire:
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def probe(name: String, wire: Wire) = {
def probeAction() = {
println(name + " " + currentTime +
" new-value = " + wire.getSignal)
}
wire addAction probeAction
}
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The effect of the probe procedure is to install a probeAction on a given
wire. As usual, the installed action is executed every time the wire’s signal
changes. In this case it simply prints the name of the wire (which is passed
as first parameter to probe), as well as the current simulated time and the
wire’s new value.
Running the simulator
After all these preparations, it’s time to see the simulator in action. To
define a concrete simulation, you need to inherit from a simulation framework class. To see something interesting, we’ll create an abstract simulation
class that extends BasicCircuitSimulation and contains method definitions for half-adders and full-adders as they were presented earlier in this
chapter in Listings 18.6 and 18.7, respectively. This class, which we’ll call
CircuitSimulation, is shown in Listing 18.11.
package org.stairwaybook.simulation
abstract class CircuitSimulation
extends BasicCircuitSimulation {
def halfAdder(a: Wire, b: Wire, s: Wire, c: Wire) = {
val d, e = new Wire
orGate(a, b, d)
andGate(a, b, c)
inverter(c, e)
andGate(d, e, s)
}
def fullAdder(a: Wire, b: Wire, cin: Wire,
sum: Wire, cout: Wire) = {
val s, c1, c2 = new Wire
halfAdder(a, cin, s, c1)
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halfAdder(b, s, sum, c2)
orGate(c1, c2, cout)
}
}
Listing 18.11 · The CircuitSimulation class.
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A concrete circuit simulation will be an object that inherits from class
CircuitSimulation. The object still needs to fix the gate delays according
to the circuit implementation technology that’s simulated. Finally, you will
also need to define the concrete circuit that’s going to be simulated.
You can do these steps interactively in the Scala interpreter:
scala> import org.stairwaybook.simulation._
import org.stairwaybook.simulation._
First, the gate delays. Define an object (call it MySimulation) that provides
some numbers:
scala> object MySimulation extends CircuitSimulation {
def InverterDelay = 1
def AndGateDelay = 3
def OrGateDelay = 5
}
defined module MySimulation
Because you are going to access the members of the MySimulation object
repeatedly, an import of the object keeps the subsequent code shorter:
scala> import MySimulation._
import MySimulation._
Next, the circuit. Define four wires, and place probes on two of them:
scala> val input1, input2, sum, carry = new Wire
input1: MySimulation.Wire =
BasicCircuitSimulation$Wire@111089b
input2: MySimulation.Wire =
BasicCircuitSimulation$Wire@14c352e
sum: MySimulation.Wire =
BasicCircuitSimulation$Wire@37a04c
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carry: MySimulation.Wire =
BasicCircuitSimulation$Wire@1fd10fa
scala> probe("sum", sum)
sum 0 new-value = false
scala> probe("carry", carry)
carry 0 new-value = false
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Note that the probes immediately print an output. This is because every
action installed on a wire is executed a first time when the action is installed.
Now define a half-adder connecting the wires:
scala> halfAdder(input1, input2, sum, carry)
Finally, set the signals, one after another, on the two input wires to true and
run the simulation:
scala> input1 setSignal true
scala> run()
*** simulation started, time = 0 ***
sum 8 new-value = true
scala> input2 setSignal true
scala> run()
*** simulation started, time = 8 ***
carry 11 new-value = true
sum 15 new-value = false
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18.7
Conclusion
This chapter brought together two techniques that seem disparate at first:
mutable state and higher-order functions. Mutable state was used to simulate
physical entities whose state changes over time. Higher-order functions were
used in the simulation framework to execute actions at specified points in
simulated time. They were also used in the circuit simulations as triggers that
associate actions with state changes. Along the way, you saw a simple way
to define a domain-specific language as a library. That’s probably enough for
one chapter!
If you feel like staying a bit longer, you might want to try more simulation examples. You can combine half-adders and full-adders to create larger
circuits, or design new circuits from the basic gates defined so far and simulate them. In the next chapter, you’ll learn about type parameterization in
Scala, and see another example in which a combination of functional and
imperative approaches yields a good solution.
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Chapter 19
Type Parameterization
In this chapter, we’ll explain the details of type parameterization in Scala.
Along the way we’ll demonstrate some of the techniques for information
hiding introduced in Chapter 13 by using a concrete example: the design of
a class for purely functional queues. We’re presenting type parameterization
and information hiding together, because information hiding can be used to
obtain more general type parameterization variance annotations.
Type parameterization allows you to write generic classes and traits. For
example, sets are generic and take a type parameter: they are defined as
Set[T]. As a result, any particular set instance might be a Set[String],
a Set[Int], etc., but it must be a set of something. Unlike Java, which
allows raw types, Scala requires that you specify type parameters. Variance
defines inheritance relationships of parameterized types, such as whether a
Set[String], for example, is a subtype of Set[AnyRef].
The chapter contains three parts. The first part develops a data structure for purely functional queues. The second part develops techniques to
hide internal representation details of this structure. The final part explains
variance of type parameters and how it interacts with information hiding.
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19.1
Functional queues
A functional queue is a data structure with three operations:
head
tail
enqueue
returns the first element of the queue
returns a queue without its first element
returns a new queue with a given element
appended at the end
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Section 19.1
Chapter 19 · Type Parameterization
Unlike a mutable queue, a functional queue does not change its contents
when an element is appended. Instead, a new queue is returned that contains
the element. The goal of this chapter will be to create a class, which we’ll
name Queue, that works like this:
scala> val q = Queue(1, 2, 3)
q: Queue[Int] = Queue(1, 2, 3)
scala> val q1 = q enqueue 4
q1: Queue[Int] = Queue(1, 2, 3, 4)
scala> q
res0: Queue[Int] = Queue(1, 2, 3)
If Queue were a mutable implementation, the enqueue operation in the second input line above would affect the contents of q; in fact both the result,
q1, and the original queue, q, would contain the sequence 1, 2, 3, 4 after
the operation. But for a functional queue, the appended value shows up only
in the result, q1, not in the queue, q, being operated on.
Purely functional queues also have some similarity with lists. Both are
so called fully persistent data structures, where old versions remain available
even after extensions or modifications. Both support head and tail operations. But where a list is usually extended at the front, using a :: operation,
a queue is extended at the end, using enqueue.
How can this be implemented efficiently? Ideally, a functional (immutable) queue should not have a fundamentally higher overhead than an
imperative (mutable) one. That is, all three operations, head, tail, and
enqueue, should operate in constant time.
One simple approach to implement a functional queue would be to use a
list as representation type. Then head and tail would just translate into the
same operations on the list, whereas enqueue would be concatenation.
This would give the following implementation:
class SlowAppendQueue[T](elems: List[T]) { // Not efficient
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def head = elems.head
def tail = new SlowAppendQueue(elems.tail)
def enqueue(x: T) = new SlowAppendQueue(elems ::: List(x))
}
The problem with this implementation is in the enqueue operation. It takes
time proportional to the number of elements stored in the queue. If you want
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constant time append, you could also try to reverse the order of the elements
in the representation list, so that the last element that’s appended comes first
in the list. This would lead to the following implementation:
class SlowHeadQueue[T](smele: List[T]) { // Not efficient
// smele is elems reversed
def head = smele.last
def tail = new SlowHeadQueue(smele.init)
def enqueue(x: T) = new SlowHeadQueue(x :: smele)
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}
Now enqueue is constant time, but head and tail are not. They now take
time proportional to the number of elements stored in the queue.
Looking at these two examples, it does not seem easy to come up with
an implementation that’s constant time for all three operations. In fact, it
looks doubtful that this is even possible! However, by combining the two
operations you can get very close. The idea is to represent a queue by two
lists, called leading and trailing. The leading list contains elements
towards the front, whereas the trailing list contains elements towards the
back of the queue in reversed order. The contents of the whole queue are at
each instant equal to “leading ::: trailing.reverse”.
Now, to append an element, you just cons it to the trailing list using
the :: operator, so enqueue is constant time. This means that, when an
initially empty queue is constructed from successive enqueue operations,
the trailing list will grow whereas the leading list will stay empty. Then,
before the first head or tail operation is performed on an empty leading
list, the whole trailing list is copied to leading, reversing the order of the
elements. This is done in an operation called mirror. Listing 19.1 shows an
implementation of queues that uses this approach.
What is the complexity of this implementation of queues? The mirror
operation might take time proportional to the number of queue elements, but
only if list leading is empty. It returns directly if leading is non-empty.
Because head and tail call mirror, their complexity might be linear in the
size of the queue, too. However, the longer the queue gets, the less often
mirror is called.
Indeed, assume a queue of length n with an empty leading list. Then
mirror has to reverse-copy a list of length n. However, the next time mirror
will have to do any work is once the leading list is empty again, which will
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class Queue[T](
private val leading: List[T],
private val trailing: List[T]
) {
private def mirror =
if (leading.isEmpty)
new Queue(trailing.reverse, Nil)
else
this
def head = mirror.leading.head
def tail = {
val q = mirror
new Queue(q.leading.tail, q.trailing)
}
def enqueue(x: T) =
new Queue(leading, x :: trailing)
}
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Listing 19.1 · A basic functional queue.
be the case after n tail operations. This means you can “charge” each of
these n tail operations with one n’th of the complexity of mirror, which
means a constant amount of work. Assuming that head, tail, and enqueue
operations appear with about the same frequency, the amortized complexity
is hence constant for each operation. So functional queues are asymptotically
just as efficient as mutable ones.
Now, there are some caveats that need to be attached to this argument.
First, the discussion was only about asymptotic behavior; the constant factors
might well be somewhat different. Second, the argument rested on the fact
that head, tail and enqueue are called with about the same frequency. If
head is called much more often than the other two operations, the argument
is not valid, as each call to head might involve a costly re-organization of the
list with mirror. The second caveat can be avoided; it is possible to design
functional queues so that in a sequence of successive head operations only
the first one might require a re-organization. You will find out at the end of
this chapter how this is done.
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Section 19.2
19.2
Chapter 19 · Type Parameterization
Information hiding
The implementation of Queue shown in Listing 19.1 is now quite good with
regards to efficiency. You might object, though, that this efficiency is paid
for by exposing a needlessly detailed implementation. The Queue constructor, which is globally accessible, takes two lists as parameters, where one is
reversed—hardly an intuitive representation of a queue. What’s needed is a
way to hide this constructor from client code. In this section, we’ll show you
some ways to accomplish this in Scala.
Private constructors and factory methods
In Java, you can hide a constructor by making it private. In Scala, the primary constructor does not have an explicit definition; it is defined implicitly
by the class parameters and body. Nevertheless, it is still possible to hide
the primary constructor by adding a private modifier in front of the class
parameter list, as shown in Listing 19.2:
class Queue[T] private (
private val leading: List[T],
private val trailing: List[T]
)
Listing 19.2 · Hiding a primary constructor by making it private.
The private modifier between the class name and its parameters indicates that the constructor of Queue is private: it can be accessed only from
within the class itself and its companion object. The class name Queue is
still public, so you can use it as a type, but you cannot call its constructor:
scala> new Queue(List(1, 2), List(3))
:9: error: constructor Queue in class Queue cannot
be accessed in object $iw
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new Queue(List(1, 2), List(3))
ˆ
Now that the primary constructor of class Queue can no longer be called
from client code, there needs to be some other way to create new queues.
One possibility is to add an auxiliary constructor, like this:
def this() = this(Nil, Nil)
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The auxiliary constructor shown in the previous example builds an empty
queue. As a refinement, the auxiliary constructor could take a list of initial
queue elements:
def this(elems: T*) = this(elems.toList, Nil)
Recall that T* is the notation for repeated parameters, as described in Section 8.8.
Another possibility is to add a factory method that builds a queue from
such a sequence of initial elements. A neat way to do this is to define an
object Queue that has the same name as the class being defined and contains
an apply method, as shown in Listing 19.3:
object Queue {
// constructs a queue with initial elements ‘xs'
def apply[T](xs: T*) = new Queue[T](xs.toList, Nil)
}
Listing 19.3 · An apply factory method in a companion object.
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By placing this object in the same source file as class Queue, you make
the object a companion object of the class. You saw in Section 13.5 that a
companion object has the same access rights as its class. Because of this, the
apply method in object Queue can create a new Queue object, even though
the constructor of class Queue is private.
Note that, because the factory method is called apply, clients can create
queues with an expression such as Queue(1, 2, 3). This expression expands
to Queue.apply(1, 2, 3) since Queue is an object instead of a function.
As a result, Queue looks to clients as if it was a globally defined factory
method. In reality, Scala has no globally visible methods; every method
must be contained in an object or a class. However, using methods named
apply inside global objects, you can support usage patterns that look like
invocations of global methods.
An alternative: private classes
Private constructors and private members are one way to hide the initialization and representation of a class. Another more radical way is to hide
the class itself and only export a trait that reveals the public interface of the
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trait Queue[T] {
def head: T
def tail: Queue[T]
def enqueue(x: T): Queue[T]
}
object Queue {
def apply[T](xs: T*): Queue[T] =
new QueueImpl[T](xs.toList, Nil)
private class QueueImpl[T](
private val leading: List[T],
private val trailing: List[T]
) extends Queue[T] {
def mirror =
if (leading.isEmpty)
new QueueImpl(trailing.reverse, Nil)
else
this
def head: T = mirror.leading.head
def tail: QueueImpl[T] = {
val q = mirror
new QueueImpl(q.leading.tail, q.trailing)
}
def enqueue(x: T) =
new QueueImpl(leading, x :: trailing)
}
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}
Listing 19.4 · Type abstraction for functional queues.
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class. The code in Listing 19.4 implements this design. There’s a trait Queue,
which declares the methods head, tail, and enqueue. All three methods are
implemented in a subclass QueueImpl, which is itself a private inner class
of object Queue. This exposes to clients the same information as before, but
using a different technique. Instead of hiding individual constructors and
methods, this version hides the whole implementation class.
19.3
Variance annotations
Queue, as defined in Listing 19.4, is a trait, but not a type. Queue is not a
type because it takes a type parameter.
As a result, you cannot create variables of type Queue:
scala> def doesNotCompile(q: Queue) = {}
:8: error: class Queue takes type parameters
def doesNotCompile(q: Queue) = {}
ˆ
Instead, trait Queue enables you to specify parameterized types, such as
Queue[String], Queue[Int], or Queue[AnyRef]:
scala> def doesCompile(q: Queue[AnyRef]) = {}
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doesCompile: (q: Queue[AnyRef])Unit
Thus, Queue is a trait and Queue[String] is a type. Queue is also called
a type constructor because you can construct a type with it by specifying a
type parameter. (This is analogous to constructing an object instance with
a plain-old constructor by specifying a value parameter.) The type constructor Queue “generates” a family of types, which includes Queue[Int],
Queue[String], and Queue[AnyRef].
You can also say that Queue is a generic trait. (Classes and traits that
take type parameters are “generic,” but the types they generate are “parameterized,” not generic.) The term “generic” means that you are defining
many specific types with one generically written class or trait. For example, trait Queue in Listing 19.4 defines a generic queue. Queue[Int] and
Queue[String], etc., would be the specific queues.
The combination of type parameters and subtyping poses some interesting questions. For example, are there any special subtyping relationships between members of the family of types generated by Queue[T]? More specifically, should a Queue[String] be considered a subtype of Queue[AnyRef]?
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Or more generally, if S is a subtype of type T, then should Queue[S] be
considered a subtype of Queue[T]? If so, you could say that trait Queue is
covariant (or “flexible”) in its type parameter T. Or, since it just has one
type parameter, you could say simply that Queues are covariant. Covariant
Queues would mean, for example, that you could pass a Queue[String] to
the doesCompile method shown previously, which takes a value parameter
of type Queue[AnyRef].
Intuitively, all this seems OK, since a queue of Strings looks like a special case of a queue of AnyRefs. In Scala, however, generic types have by
default nonvariant (or “rigid”) subtyping. That is, with Queue defined as in
Listing 19.4, queues with different element types would never be in a subtype
relationship. A Queue[String] would not be usable as a Queue[AnyRef].
However, you can demand covariant (flexible) subtyping of queues by changing the first line of the definition of class Queue like this:
trait Queue[+T] { ... }
Prefixing a formal type parameter with a + indicates that subtyping is covariant (flexible) in that parameter. By adding this single character, you are
telling Scala that you want Queue[String], for example, to be considered a
subtype of Queue[AnyRef]. The compiler will check that Queue is defined
in a way that this subtyping is sound.
Besides +, there is also a prefix -, which indicates contravariant subtyping. If Queue were defined like this:
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trait Queue[-T] { ... }
then if T is a subtype of type S, this would imply that Queue[S] is a subtype of Queue[T] (which in the case of queues would be rather surprising!).
Whether a type parameter is covariant, contravariant, or nonvariant is called
the parameter’s variance . The + and - symbols you can place next to type
parameters are called variance annotations.
In a purely functional world, many types are naturally covariant (flexible). However, the situation changes once you introduce mutable data. To
find out why, consider the simple type of one-element cells that can be read
or written, shown in Listing 19.5.
The Cell type of Listing 19.5 is declared nonvariant (rigid). For the
sake of argument, assume for a moment that Cell was declared covariant
instead—i.e., it was declared class Cell[+T]—and that this passed the
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class Cell[T](init: T) {
private[this] var current = init
def get = current
def set(x: T) = { current = x }
}
Listing 19.5 · A nonvariant (rigid) Cell class.
Scala compiler. (It doesn’t, and we’ll explain why shortly.) Then you could
construct the following problematic statement sequence:
val c1 = new Cell[String]("abc")
val c2: Cell[Any] = c1
c2.set(1)
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val s: String = c1.get
Seen by itself, each of these four lines looks OK. The first line creates a cell
of strings and stores it in a val named c1. The second line defines a new
val, c2, of type Cell[Any], which initialized with c1. This is OK since
Cells are assumed to be covariant. The third line sets the value of cell c2
to 1. This is also OK because the assigned value 1 is an instance of c2’s
element type Any. Finally, the last line assigns the element value of c1 into
a string. Nothing strange here, as both the sides are of the same type. But
taken together, these four lines end up assigning the integer 1 to the string s.
This is clearly a violation of type soundness.
Which operation is to blame for the runtime fault? It must be the second
one, which uses covariant subtyping. The other statements are too simple
and fundamental. Thus, a Cell of String is not also a Cell of Any, because
there are things you can do with a Cell of Any that you cannot do with a
Cell of String. You cannot use set with an Int argument on a Cell of
String, for example.
In fact, were you to pass the covariant version of Cell to the Scala compiler, you would get a compile-time error:
Cell.scala:7: error: covariant type T occurs in
contravariant position in type T of value x
def set(x: T) = current = x
ˆ
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Variance and arrays
It’s interesting to compare this behavior with arrays in Java. In principle,
arrays are just like cells except that they can have more than one element.
Nevertheless, arrays are treated as covariant in Java.
You can try an example analogous to the cell interaction described here
with Java arrays:
// this is Java
String[] a1 = { "abc" };
Object[] a2 = a1;
a2[0] = new Integer(17);
String s = a1[0];
If you try out this example, you will find that it compiles. But executing the
program will cause an ArrayStore exception to be thrown when a2[0] is
assigned to an Integer:
Exception in thread "main" java.lang.ArrayStoreException:
java.lang.Integer
at JavaArrays.main(JavaArrays.java:8)
What happens here is that Java stores the element type of the array at runtime. Then, every time an array element is updated, the new element value
is checked against the stored type. If it is not an instance of that type, an
ArrayStore exception is thrown.
You might ask why Java adopted this design, which seems both unsafe
and expensive. When asked this question, James Gosling, the principal inventor of the Java language, answered that they wanted to have a simple
means to treat arrays generically. For instance, they wanted to be able to
write a method to sort all elements of an array, using a signature like the
following that takes an array of Object:
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void sort(Object[] a, Comparator cmp) { ... }
Covariance of arrays was needed so that arrays of arbitrary reference types
could be passed to this sort method. Of course, with the arrival of Java
generics, such a sort method can now be written with a type parameter, so
the covariance of arrays is no longer necessary. For compatibility reasons,
though, it has persisted in Java to this day.
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Scala tries to be purer than Java in not treating arrays as covariant. Here’s
what you get if you translate the first two lines of the array example to Scala:
scala> val a1 = Array("abc")
a1: Array[String] = Array(abc)
scala> val a2: Array[Any] = a1
:8: error: type mismatch;
found
: Array[String]
required: Array[Any]
val a2: Array[Any] = a1
ˆ
What happened here is that Scala treats arrays as nonvariant (rigid), so an
Array[String] is not considered to conform to an Array[Any]. However,
sometimes it is necessary to interact with legacy methods in Java that use an
Object array as a means to emulate a generic array. For instance, you might
want to call a sort method like the one described previously with an array
of Strings as argument. To make this possible, Scala lets you cast an array
of Ts to an array of any supertype of T:
scala> val a2: Array[Object] =
a1.asInstanceOf[Array[Object]]
a2: Array[Object] = Array(abc)
The cast is always legal at compile-time, and it will always succeed at runtime because the JVM’s underlying run-time model treats arrays as covariant,
just as Java the language does. But you might get ArrayStore exceptions
afterwards, again just as you would in Java.
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19.4
Checking variance annotations
Now that you have seen some examples where variance is unsound, you may
be wondering which kind of class definitions need to be rejected and which
can be accepted. So far, all violations of type soundness involved some reassignable field or array element. The purely functional implementation of
queues, on the other hand, looks like a good candidate for covariance. However, the following example shows that you can “engineer” an unsound situation even if there is no reassignable field.
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To set up the example, assume that queues as defined in Listing 19.4 are
covariant. Then, create a subclass of queues that specializes the element type
to Int and overrides the enqueue method:
class StrangeIntQueue extends Queue[Int] {
override def enqueue(x: Int) = {
println(math.sqrt(x))
super.enqueue(x)
}
}
The enqueue method in StrangeIntQueue prints out the square root of its
(integer) argument before doing the append proper.
Now, you can write a counterexample in two lines:
val x: Queue[Any] = new StrangeIntQueue
x.enqueue("abc")
The first of these two lines is valid because StrangeIntQueue is a subclass
of Queue[Int] and, assuming covariance of queues, Queue[Int] is a subtype of Queue[Any]. The second line is valid because you can append a
String to a Queue[Any]. However, taken together, these two lines have the
effect of applying a square root method to a string, which makes no sense.
Clearly it’s not just mutable fields that make covariant types unsound.
The problem is more general. It turns out that as soon as a generic parameter
type appears as the type of a method parameter, the containing class or trait
may not be covariant in that type parameter.
For queues, the enqueue method violates this condition:
class Queue[+T] {
def enqueue(x: T) =
...
Prepared for Library Genesis
}
Running a modified queue class like the one above through a Scala compiler
would yield:
Queues.scala:11: error: covariant type T occurs in
contravariant position in type T of value x
def enqueue(x: T) =
ˆ
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Reassignable fields are a special case of the rule that disallows type parameters annotated with + from being used as method parameter types. As mentioned in Section 18.2, a reassignable field, “var x: T”, is treated in Scala as
a getter method, “def x: T”, and a setter method, “def x_=(y: T)”. As you
can see, the setter method has a parameter of the field’s type T. So that type
may not be covariant.
The fast track
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In the rest of this section, we’ll describe the mechanism by which the
Scala compiler checks variance annotations. If you’re not interested in
such detail right now, you can safely skip to Section 19.5. The most
important thing to understand is that the Scala compiler will check any
variance annotations you place on type parameters. For example, if you try
to declare a type parameter to be covariant (by adding a +), but that could
lead to potential runtime errors, your program won’t compile.
To verify correctness of variance annotations, the Scala compiler classifies all positions in a class or trait body as positive, negative or neutral.
A “position” is any location in the class or trait (but from now on we’ll just
write “class”) body where a type parameter may be used. For example, every
method value parameter is a position because a method value parameter has
a type. Therefore a type parameter could appear in that position.
The compiler checks each use of each of the class’s type parameters.
Type parameters annotated with + may only be used in positive positions,
while type parameters annotated with - may only be used in negative positions. A type parameter with no variance annotation may be used in any
position, and is, therefore, the only kind of type parameter that can be used
in neutral positions of the class body.
To classify the positions, the compiler starts from the declaration of a
type parameter and then moves inward through deeper nesting levels. Positions at the top level of the declaring class are classified as positive. By
default, positions at deeper nesting levels are classified the same as that at
enclosing levels, but there are a handful of exceptions where the classification changes. Method value parameter positions are classified to the flipped
classification relative to positions outside the method, where the flip of a positive classification is negative, the flip of a negative classification is positive,
and the flip of a neutral classification is still neutral.
Besides method value parameter positions, the current classification is
also flipped at the type parameters of methods. A classification is sometimes
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Chapter 19 · Type Parameterization
flipped at the type argument position of a type, such as the Arg in C[Arg],
depending on the variance of the corresponding type parameter. If C’s type
parameter is annotated with a + then the classification stays the same. If
C’s type parameter is annotated with a -, then the current classification is
flipped. If C’s type parameter has no variance annotation then the current
classification is changed to neutral.
As a somewhat contrived example, consider the following class definition, where several positions are annotated with their classifications, + (for
positive) or − (for negative):
abstract class Cat[-T, +U] {
def meow[W− ](volume: T− , listener: Cat[U+ , T− ]− )
: Cat[Cat[U+ , T− ]− , U+ ]+
}
The positions of the type parameter, W, and the two value parameters,
volume and listener, are all negative. Looking at the result type of meow,
the position of the first Cat[U, T] argument is negative because Cat’s first
type parameter, T, is annotated with a -. The type U inside this argument is
again in positive position (two flips), whereas the type T inside that argument
is still in negative position.
You see from this discussion that it’s quite hard to keep track of variance
positions. That’s why it’s a welcome relief that the Scala compiler does this
job for you.
Once the classifications are computed, the compiler checks that each type
parameter is only used in positions that are classified appropriately. In this
case, T is only used in negative positions, and U is only used in positive
positions. So class Cat is type correct.
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19.5
Lower bounds
Back to the Queue class. You saw that the previous definition of Queue[T]
shown in Listing 19.4 cannot be made covariant in T because T appears as a
type of a parameter of the enqueue method, and that’s a negative position.
Fortunately, there’s a way to get unstuck: you can generalize enqueue
by making it polymorphic (i.e., giving the enqueue method itself a type parameter) and using a lower bound for its type parameter. Listing 19.6 shows
a new formulation of Queue that implements this idea.
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class Queue[+T] (private val leading: List[T],
private val trailing: List[T] ) {
def enqueue[U >: T](x: U) =
new Queue[U](leading, x :: trailing) // ...
}
Listing 19.6 · A type parameter with a lower bound.
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The new definition gives enqueue a type parameter U, and with the syntax,
“U >: T”, defines T as the lower bound for U. As a result, U is required to be a
supertype of T.1 The parameter to enqueue is now of type U instead of type
T, and the return value of the method is now Queue[U] instead of Queue[T].
For example, suppose there is a class Fruit with two subclasses, Apple
and Orange. With the new definition of class Queue, it is possible to append
an Orange to a Queue[Apple]. The result will be a Queue[Fruit].
This revised definition of enqueue is type correct. Intuitively, if T is a
more specific type than expected (for example, Apple instead of Fruit), a
call to enqueue will still work because U (Fruit) will still be a supertype of
T (Apple).2
The new definition of enqueue is arguably better than the old, because
it is more general. Unlike the old version, the new definition allows you to
append an arbitrary supertype U of the queue element type T. The result is
then a Queue[U]. Together with queue covariance, this gives the right kind
of flexibility for modeling queues of different element types in a natural way.
This shows that variance annotations and lower bounds play well together. They are a good example of type-driven design, where the types of
an interface guide its detailed design and implementation. In the case of
queues, it’s likely you would not have thought of the refined implementation
of enqueue with a lower bound. But you might have decided to make queues
covariant, in which case, the compiler would have pointed out the variance
error for enqueue. Correcting the variance error by adding a lower bound
makes enqueue more general and queues as a whole more usable.
1 Supertype
and subtype relationships are reflexive, which means a type is both a supertype and a subtype of itself. Even though T is a lower bound for U, you could still pass in a T
to enqueue.
2 Technically, what happens is a flip occurs for lower bounds. The type parameter U is in
a negative position (1 flip), while the lower bound (>: T) is in a positive position (2 flips).
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This observation is also the main reason that Scala prefers declarationsite variance over use-site variance as it is found in Java’s wildcards. With
use-site variance, you are on your own designing a class. It will be the clients
of the class that need to put in the wildcards, and if they get it wrong, some
important instance methods will no longer be applicable. Variance being a
tricky business, users usually get it wrong, and they come away thinking that
wildcards and generics are overly complicated. With definition-side variance, you express your intent to the compiler, and the compiler will double
check that the methods you want available will indeed be available.
19.6
Contravariance
So far in this chapter, all examples you’ve seen were either covariant or nonvariant. But there are also cases where contravariance is natural. For instance, consider the trait of output channels shown in Listing 19.7:
trait OutputChannel[-T] {
def write(x: T)
}
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Listing 19.7 · A contravariant output channel.
Here, OutputChannel is defined to be contravariant in T. So an output channel of AnyRefs, say, is a subtype of an output channel of Strings. Although it may seem non-intuitive, it actually makes sense. To see why, consider what you can do with an OutputChannel[String]. The only supported operation is writing a String to it. The same operation can also
be done on an OutputChannel[AnyRef]. So it is safe to substitute an
OutputChannel[AnyRef] for an OutputChannel[String]. By contrast,
it would not be safe to substitute an OutputChannel[String] where an
OutputChannel[AnyRef] is required. After all, you can send any object
to an OutputChannel[AnyRef], whereas an OutputChannel[String] requires that the written values are all strings.
This reasoning points to a general principle in type system design: It is
safe to assume that a type T is a subtype of a type U if you can substitute
a value of type T wherever a value of type U is required. This is called the
Liskov Substitution Principle. The principle holds if T supports the same
operations as U, and all of T’s operations require less and provide more
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trait Function1[-S, +T] {
def apply(x: S): T
}
Listing 19.8 · Covariance and contravariance of Function1s.
Prepared for Library Genesis
than the corresponding operations in U. In the case of output channels, an
OutputChannel[AnyRef] can be a subtype of an OutputChannel[String]
because the two support the same write operation, and this operation requires less in OutputChannel[AnyRef] than in OutputChannel[String].
“Less” means the argument is only required to be an AnyRef in the first case,
whereas it is required to be a String in the second case.
Sometimes covariance and contravariance are mixed in the same type.
A prominent example is Scala’s function traits. For instance, whenever you
write the function type A => B, Scala expands this to Function1[A, B]. The
definition of Function1 in the standard library uses both covariance and
contravariance: the Function1 trait is contravariant in the function argument
type S and covariant in the result type T, as shown in Listing 19.8. This
satisfies the Liskov Substitution Principle because arguments are something
that’s required, whereas results are something that’s provided.
As an example, consider the application shown in Listing 19.9. Here,
class Publication contains one parametric field, title, of type String.
Class Book extends Publication and forwards its string title parameter
to the constructor of its superclass. The Library singleton object defines a
set of books and a method printBookList, which takes a function, named
info, of type Book => AnyRef. In other words, the type of the lone parameter
to printBookList is a function that takes one Book argument and returns
an AnyRef. The Customer application defines a method, getTitle, which
takes a Publication as its lone parameter and returns a String, the title of
the passed Publication.
Now take a look at the last line in Customer. This line invokes Library’s
printBookList method and passes getTitle, wrapped in a function value:
Library.printBookList(getTitle)
This line of code type checks even though String, the function’s result type,
is a subtype of AnyRef, the result type of printBookList’s info parameter. This code passes the compiler because function result types are deCover · Overview · Contents · Discuss · Suggest · Glossary · Index
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class Publication(val title: String)
class Book(title: String) extends Publication(title)
object Library {
val books: Set[Book] =
Set(
new Book("Programming in Scala"),
new Book("Walden")
)
def printBookList(info: Book => AnyRef) = {
for (book <- books) println(info(book))
}
}
object Customer extends App {
def getTitle(p: Publication): String = p.title
Library.printBookList(getTitle)
}
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Listing 19.9 · Demonstration of function type parameter variance.
clared to be covariant (the +T in Listing 19.8). If you look inside the body of
printBookList, you can get a glimpse of why this makes sense.
The printBookList method iterates through its book list and invokes
the passed function on each book. It passes the AnyRef result returned by
info to println, which invokes toString on it and prints the result. This
activity will work with String as well as any other subclass of AnyRef,
which is what covariance of function result types means.
Now consider the parameter type of the function being passed to the
printBookList method. Although printBookList’s parameter type is declared as Book, the getTitle we’re passing in takes a Publication, a supertype of Book. The reason this works is that since printBookList’s parameter type is Book, the body of the printBookList method will only be
allowed to pass a Book into the function. And because getTitle’s parameter
type is Publication, the body of that function will only be able to access on
its parameter, p, members that are declared in class Publication. Because
any method declared in Publication is also available on its subclass Book,
everything should work, which is what contravariance of function parameter
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Section 19.7
Chapter 19 · Type Parameterization
argument type
Book => AnyRef
Book
Publication
437
result type
AnyRef
Publication => String
String
Figure 19.1 · Covariance and contravariance in function type parameters.
types means. You can see all this graphically in Figure 19.1.
The code in Listing 19.9 compiles because Publication => String is
a subtype of Book => AnyRef, as shown in the center of the Figure 19.1. Because the result type of a Function1 is defined as covariant, the inheritance
relationship of the two result types, shown at the right of the diagram, is in
the same direction as that of the two functions shown in the center. By contrast, because the parameter type of a Function1 is defined as contravariant,
the inheritance relationship of the two parameter types, shown at the left of
the diagram, is in the opposite direction as that of the two functions.
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19.7
Object private data
The Queue class seen so far has a problem in that the mirror operation will
repeatedly copy the trailing into the leading list if head is called several
times in a row on a list where leading is empty. The wasteful copying could
be avoided by adding some judicious side effects. Listing 19.10 presents a
new implementation of Queue, which performs at most one trailing to
leading adjustment for any sequence of head operations.
What’s different with respect to the previous version is that now leading
and trailing are reassignable variables, and mirror performs the reverse
copy from trailing to leading as a side effect on the current queue instead
of returning a new queue. This side effect is purely internal to the implementation of the Queue operation; since leading and trailing are private
variables, the effect is not visible to clients of Queue. So by the terminology
established in Chapter 18, the new version of Queue still defines purely functional objects, in spite of the fact that they now contain reassignable fields.
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Section 19.7
Chapter 19 · Type Parameterization
class Queue[+T] private (
private[this] var leading: List[T],
private[this] var trailing: List[T]
) {
private def mirror() =
if (leading.isEmpty) {
while (!trailing.isEmpty) {
leading = trailing.head :: leading
trailing = trailing.tail
}
}
def head: T = {
mirror()
leading.head
}
def tail: Queue[T] = {
mirror()
new Queue(leading.tail, trailing)
}
def enqueue[U >: T](x: U) =
new Queue[U](leading, x :: trailing)
}
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Listing 19.10 · An optimized functional queue.
You might wonder whether this code passes the Scala type checker. After
all, queues now contain two reassignable fields of the covariant parameter
type T. Is this not a violation of the variance rules? It would be indeed,
except for the detail that leading and trailing have a private[this]
modifier, and are thus declared to be object private.
As mentioned in Section 13.5, object private members can be accessed
only from within the object in which they are defined. It turns out that accesses to variables from the same object in which they are defined do not
cause problems with variance. The intuitive explanation is that, in order to
construct a case where variance would lead to type errors, you need to have a
reference to a containing object that has a statically weaker type than the type
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Section 19.8
Chapter 19 · Type Parameterization
the object was defined with. For accesses to object private values, however,
this is impossible.
Scala’s variance checking rules contain a special case for object private
definitions. Such definitions are omitted when it is checked that a type parameter with either a + or - annotation occurs only in positions that have the
same variance classification. Therefore, the code in Listing 19.10 compiles
without error. On the other hand, if you had left out the [this] qualifiers
from the two private modifiers, you would see two type errors:
Queues.scala:1: error: covariant type T occurs in
contravariant position in type List[T] of parameter of
setter leading_=
class Queue[+T] private (private var leading: List[T],
ˆ
Queues.scala:1: error: covariant type T occurs in
contravariant position in type List[T] of parameter of
setter trailing_=
private var trailing: List[T]) {
ˆ
19.8
Upper bounds
In Listing 16.1 on page 356, we showed a merge sort function for lists that
took a comparison function as a first argument and a list to sort as a second, curried argument. Another way you might want to organize such a sort
function is by requiring the type of the list to mix in the Ordered trait. As
mentioned in Section 12.4, by mixing Ordered into a class and implementing Ordered’s one abstract method, compare, you enable clients to compare
instances of that class with <, >, <=, and >=. For example, Listing 19.11
shows Ordered being mixed into a Person class.
As a result, you can compare two people like this:
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scala> val robert = new Person("Robert", "Jones")
robert: Person = Robert Jones
scala> val sally = new Person("Sally", "Smith")
sally: Person = Sally Smith
scala> robert < sally
res0: Boolean = true
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Section 19.8
Chapter 19 · Type Parameterization
class Person(val firstName: String, val lastName: String)
extends Ordered[Person] {
def compare(that: Person) = {
val lastNameComparison =
lastName.compareToIgnoreCase(that.lastName)
if (lastNameComparison != 0)
lastNameComparison
else
firstName.compareToIgnoreCase(that.firstName)
}
override def toString = firstName + " " + lastName
}
Listing 19.11 · A Person class that mixes in the Ordered trait.
def orderedMergeSort[T <: Ordered[T]](xs: List[T]): List[T] = {
def merge(xs: List[T], ys: List[T]): List[T] =
(xs, ys) match {
case (Nil, _) => ys
case (_, Nil) => xs
case (x :: xs1, y :: ys1) =>
if (x < y) x :: merge(xs1, ys)
else y :: merge(xs, ys1)
}
val n = xs.length / 2
if (n == 0) xs
else {
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val (ys, zs) = xs splitAt n
merge(orderedMergeSort(ys), orderedMergeSort(zs))
}
}
Listing 19.12 · A merge sort function with an upper bound.
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To require that the type of the list passed to your new sort function mixes
in Ordered, you need to use an upper bound. An upper bound is specified
similar to a lower bound, except instead of the >: symbol used for lower
bounds, you use a <: symbol, as shown in Listing 19.12.
With the “T <: Ordered[T]” syntax, you indicate that the type parameter, T, has an upper bound, Ordered[T]. This means that the element type of
the list passed to orderedMergeSort must be a subtype of Ordered. Thus,
you could pass a List[Person] to orderedMergeSort because Person
mixes in Ordered.
For example, consider this list:
scala> val people = List(
new Person("Larry", "Wall"),
new Person("Anders", "Hejlsberg"),
new Person("Guido", "van Rossum"),
new Person("Alan", "Kay"),
new Person("Yukihiro", "Matsumoto")
)
people: List[Person] = List(Larry Wall, Anders Hejlsberg,
Guido van Rossum, Alan Kay, Yukihiro Matsumoto)
Because the element type of this list, Person, mixes in (and is therefore a
subtype of) Ordered[People], you can pass the list to orderedMergeSort:
scala> val sortedPeople = orderedMergeSort(people)
sortedPeople: List[Person] = List(Anders Hejlsberg, Alan Kay,
Yukihiro Matsumoto, Guido van Rossum, Larry Wall)
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Now, although the sort function shown in Listing 19.12 serves as a useful
illustration of upper bounds, it isn’t actually the most general way in Scala
to design a sort function that takes advantage of the Ordered trait.
For example, you couldn’t use the orderedMergeSort function to sort
a list of integers, because class Int is not a subtype of Ordered[Int]:
scala> val wontCompile = orderedMergeSort(List(3, 2, 1))
:5: error: inferred type arguments [Int] do
not conform to method orderedMergeSort's type
parameter bounds [T <: Ordered[T]]
val wontCompile = orderedMergeSort(List(3, 2, 1))
ˆ
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Section 19.9
Chapter 19 · Type Parameterization
In Section 21.6, we’ll show you how to use implicit parameters and context
bounds to achieve a more general solution.
19.9
Conclusion
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In this chapter you saw several techniques for information hiding: private
constructors, factory methods, type abstraction, and object private members.
You also learned how to specify data type variance and what it implies for
class implementation. Finally, you saw two techniques which help in obtaining flexible variance annotations: lower bounds for method type parameters
and private[this] annotations for local fields and methods.
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442
Chapter 20
Abstract Members
A member of a class or trait is abstract if the member does not have a complete definition in the class. Abstract members are intended to be implemented in subclasses of the class in which they are declared. This idea is
found in many object-oriented languages. For instance, Java lets you declare
abstract methods. Scala also lets you declare such methods, as you saw in
Section 10.2. But Scala goes beyond that and implements the idea in its
full generality: Besides methods, you can declare abstract fields and even
abstract types as members of classes and traits.
In this chapter we’ll describe all four kinds of abstract member: vals,
vars, methods, and types. Along the way we’ll discuss pre-initialized fields,
lazy vals, path-dependent types, and enumerations.
20.1
A quick tour of abstract members
The following trait declares one of each kind of abstract member: an abstract
type (T), method (transform), val (initial), and var (current):
trait Abstract {
type T
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def transform(x: T): T
val initial: T
var current: T
}
A concrete implementation of Abstract needs to fill in definitions for each
of its abstract members. Here is an example implementation that provides
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Section 20.2
Chapter 20 · Abstract Members
these definitions:
class Concrete extends Abstract {
type T = String
def transform(x: String) = x + x
val initial = "hi"
var current = initial
}
The implementation gives a concrete meaning to the type name T by defining
it as an alias of type String. The transform operation concatenates a given
string with itself, and the initial and current values are both set to "hi".
This example gives you a rough first idea of what kinds of abstract members exist in Scala. The remainder of the chapter will present the details and
explain what the new forms of abstract members, as well as type members
in general, are good for.
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20.2
Type members
As you can see from the example in the previous section, the term abstract
type in Scala means a type declared (with the “type” keyword) to be a member of a class or trait, without specifying a definition. Classes themselves
may be abstract, and traits are by definition abstract, but neither of these are
what are referred to as abstract types in Scala. An abstract type in Scala is
always a member of some class or trait, such as type T in trait Abstract.
You can think of a non-abstract (or “concrete”) type member, such as
type T in class Concrete, as a way to define a new name, or alias, for a
type. In class Concrete, for example, the type String is given the alias
T. As a result, anywhere T appears in the definition of class Concrete, it
means String. This includes the parameter and result types of transform,
initial, and current, which mention T when they are declared in supertrait Abstract. Thus, when class Concrete implements these methods,
those Ts are interpreted to mean String.
One reason to use a type member is to define a short, descriptive alias
for a type whose real name is more verbose, or less obvious in meaning, than
the alias. Such type members can help clarify the code of a class or trait.
The other main use of type members is to declare abstract types that must
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Section 20.3
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be defined in subclasses. This use, which was demonstrated in the previous
section, will be described in detail later in this chapter.
20.3
Abstract vals
An abstract val declaration has a form like:
val initial: String
It gives a name and type for a val, but not its value. This value has to
be provided by a concrete val definition in a subclass. For instance, class
Concrete implemented the val using:
val initial = "hi"
You use an abstract val declaration in a class when you do not know the
correct value in the class, but you do know that the variable will have an
unchangeable value in each instance of the class.
An abstract val declaration resembles an abstract parameterless method
declaration such as:
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def initial: String
Client code would refer to both the val and the method in exactly the same
way (i.e., obj.initial). However, if initial is an abstract val, the client
is guaranteed that obj.initial will yield the same value every time it is
referenced. If initial were an abstract method, that guarantee would not
hold because, in that case, initial could be implemented by a concrete
method that returns a different value every time it’s called.
In other words, an abstract val constrains its legal implementation: Any
implementation must be a val definition; it may not be a var or a def.
Abstract method declarations, on the other hand, may be implemented by
both concrete method definitions and concrete val definitions. Given the
abstract class Fruit shown in Listing 20.1, class Apple would be a legal
subclass implementation, but class BadApple would not.
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abstract class Fruit {
val v: String // ‘v' for value
def m: String // ‘m' for method
}
abstract class Apple extends Fruit {
val v: String
val m: String // OK to override a ‘def' with a ‘val'
}
abstract class BadApple extends Fruit {
def v: String // ERROR: cannot override a ‘val' with a ‘def'
def m: String
}
Listing 20.1 · Overriding abstract vals and parameterless methods.
20.4
Abstract vars
Like an abstract val, an abstract var declares just a name and a type, but
not an initial value. For instance, Listing 20.2 shows a trait AbstractTime,
which declares two abstract variables named hour and minute:
trait AbstractTime {
var hour: Int
var minute: Int
}
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Listing 20.2 · Declaring abstract vars.
What is the meaning of abstract vars like hour and minute? You saw in
Section 18.2 that vars declared as members of classes come equipped with
getter and setter methods. This holds for abstract vars as well. If you declare
an abstract var named hour, for example, you implicitly declare an abstract
getter method, hour, and an abstract setter method, hour_=. There’s no
reassignable field to be defined—that will come in subclasses that define the
concrete implementation of the abstract var. For instance, the definition of
AbstractTime shown in Listing 20.2 is exactly equivalent to the definition
shown in Listing 20.3.
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trait AbstractTime {
def hour: Int
// getter for ‘hour'
def hour_=(x: Int)
// setter for ‘hour'
def minute: Int
// getter for ‘minute'
def minute_=(x: Int)
// setter for ‘minute'
}
Listing 20.3 · How abstract vars are expanded into getters and setters.
20.5
Initializing abstract vals
Abstract vals sometimes play a role analogous to superclass parameters:
they let you provide details in a subclass that are missing in a superclass. This
is particularly important for traits, because traits don’t have a constructor to
which you could pass parameters. So the usual notion of parameterizing a
trait works via abstract vals that are implemented in subclasses.
As an example, consider a reformulation of class Rational from Chapter 6, as shown in Listing 6.5 on page 151, as a trait:
trait RationalTrait {
val numerArg: Int
val denomArg: Int
}
The Rational class from Chapter 6 had two parameters: n for the numerator
of the rational number, and d for the denominator. The RationalTrait
trait given here defines instead two abstract vals: numerArg and denomArg.
To instantiate a concrete instance of that trait, you need to implement the
abstract val definitions. Here’s an example:
new RationalTrait {
val numerArg = 1
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val denomArg = 2
}
Here the keyword new appears in front of a trait name, RationalTrait,
which is followed by a class body in curly braces. This expression yields an
instance of an anonymous class that mixes in the trait and is defined by the
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Section 20.5
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body. This particular anonymous class instantiation has an effect analogous
to the instance creation new Rational(1, 2).
The analogy is not perfect, however. There’s a subtle difference concerning the order in which expressions are initialized. When you write:
new Rational(expr1, expr2)
the two expressions, expr1 and expr2, are evaluated before class Rational
is initialized, so the values of expr1 and expr2 are available for the initialization of class Rational.
For traits, the situation is the opposite. When you write:
new RationalTrait {
val numerArg = expr1
val denomArg = expr2
}
the expressions, expr1 and expr2, are evaluated as part of the initialization of the anonymous class, but the anonymous class is initialized after the
RationalTrait. So the values of numerArg and denomArg are not available during the initialization of RationalTrait (more precisely, a selection
of either value would yield the default value for type Int, 0). For the definition of RationalTrait given previously, this is not a problem, because
the trait’s initialization does not make use of values numerArg or denomArg.
However, it becomes a problem in the variant of RationalTrait shown in
Listing 20.4, which defines normalized numerators and denominators.
If you try to instantiate this trait with some numerator and denominator
expressions that are not simple literals, you’ll get an exception:
scala> val x = 2
x: Int = 2
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scala> new RationalTrait {
val numerArg = 1 * x
val denomArg = 2 * x
}
java.lang.IllegalArgumentException: requirement failed
at scala.Predef$.require(Predef.scala:207)
at RationalTrait$class.$init$(:10)
... 28 elided
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Section 20.5
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trait RationalTrait {
val numerArg: Int
val denomArg: Int
require(denomArg != 0)
private val g = gcd(numerArg, denomArg)
val numer = numerArg / g
val denom = denomArg / g
private def gcd(a: Int, b: Int): Int =
if (b == 0) a else gcd(b, a % b)
override def toString = numer + "/" + denom
}
Listing 20.4 · A trait that uses its abstract vals.
The exception in this example was thrown because denomArg still had its
default value of 0 when class RationalTrait was initialized, which caused
the require invocation to fail.
This example demonstrates that initialization order is not the same for
class parameters and abstract fields. A class parameter argument is evaluated
before it is passed to the class constructor (unless the parameter is by-name).
An implementing val definition in a subclass, by contrast, is evaluated only
after the superclass has been initialized.
Now that you understand why abstract vals behave differently from parameters, it would be good to know what can be done about this. Is it possible
to define a RationalTrait that can be initialized robustly, without fearing
errors due to uninitialized fields? In fact, Scala offers two alternative solutions to this problem, pre-initialized fields and lazy vals. They are presented
in the remainder of this section.
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Pre-initialized fields
The first solution, pre-initialized fields, lets you initialize a field of a subclass
before the superclass is called. To do this, simply place the field definition
in braces before the superclass constructor call. As an example, Listing 20.5
shows another attempt to create an instance of RationalTrait. As you see
from this example, the initialization section comes before the mention of the
supertrait RationalTrait. Both are separated by a with.
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scala> new {
val numerArg = 1 * x
val denomArg = 2 * x
} with RationalTrait
res1: RationalTrait = 1/2
Listing 20.5 · Pre-initialized fields in an anonymous class expression.
Pre-initialized fields are not restricted to anonymous classes; they can
also be used in objects or named subclasses. Two examples are shown
in Listings 20.6 and 20.7. As you can see from these examples, the preinitialization section comes in each case after the extends keyword of the
defined object or class. Class RationalClass, shown in Listing 20.7, exemplifies a general schema of how class parameters can be made available for
the initialization of a supertrait.
object twoThirds extends {
val numerArg = 2
val denomArg = 3
} with RationalTrait
Listing 20.6 · Pre-initialized fields in an object definition.
class RationalClass(n: Int, d: Int) extends {
val numerArg = n
val denomArg = d
} with RationalTrait {
def + (that: RationalClass) = new RationalClass(
numer * that.denom + that.numer * denom,
denom * that.denom
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)
}
Listing 20.7 · Pre-initialized fields in a class definition.
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structed. Consequently, if such an initializer refers to this, the reference
goes to the object containing the class or object that’s being constructed, not
the constructed object itself.
Here’s an example:
scala> new {
val numerArg = 1
val denomArg = this.numerArg * 2
} with RationalTrait
:11: error: value numerArg is not a member of object
$iw
val denomArg = this.numerArg * 2
ˆ
The example did not compile because the reference this.numerArg was
looking for a numerArg field in the object containing the new (which in this
case was the synthetic object named $iw, into which the interpreter puts user
input lines). Once more, pre-initialized fields behave in this respect like class
constructor arguments.
Lazy vals
You can use pre-initialized fields to simulate precisely the initialization behavior of class constructor arguments. Sometimes, however, you might prefer to let the system itself sort out how things should be initialized. This can
be achieved by making your val definitions lazy. If you prefix a val definition with a lazy modifier, the initializing expression on the right-hand side
will only be evaluated the first time the val is used.
For an example, define an object Demo with a val as follows:
scala> object Demo {
val x = { println("initializing x"); "done" }
}
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defined object Demo
Now, first refer to Demo, then to Demo.x:
scala> Demo
initializing x
res3: Demo.type = Demo$@2129a843
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scala> Demo.x
res4: String = done
As you can see, the moment you use Demo, its x field becomes initialized.
The initialization of x forms part of the initialization of Demo. The situation
changes, however, if you define the x field to be lazy:
scala> object Demo {
lazy val x = { println("initializing x"); "done" }
}
defined object Demo
scala> Demo
res5: Demo.type = Demo$@5b1769c
scala> Demo.x
initializing x
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res6: String = done
Now, initializing Demo does not involve initializing x. The initialization of x
will be deferred until the first time x is used. This is similar to the situation
where x is defined as a parameterless method, using a def. However, unlike
a def, a lazy val is never evaluated more than once. In fact, after the first
evaluation of a lazy val the result of the evaluation is stored, to be reused
when the same val is used subsequently.
Looking at this example, it seems that objects like Demo themselves behave like lazy vals, in that they are also initialized on demand, the first time
they are used. This is correct. In fact an object definition can be seen as
a shorthand for the definition of a lazy val with an anonymous class that
describes the object’s contents.
Using lazy vals, you could reformulate RationalTrait as shown in
Listing 20.8. In the new trait definition, all concrete fields are defined lazy.
Another change with respect to the previous definition of RationalTrait,
shown in Listing 20.4, is that the require clause was moved from the body
of the trait to the initializer of the private field, g, which computes the greatest
common divisor of numerArg and denomArg. With these changes, there’s
nothing that remains to be done when LazyRationalTrait is initialized; all
initialization code is now part of the right-hand side of a lazy val. Thus, it
is safe to initialize the abstract fields of LazyRationalTrait after the class
is defined.
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trait LazyRationalTrait {
val numerArg: Int
val denomArg: Int
lazy val numer = numerArg / g
lazy val denom = denomArg / g
override def toString = numer + "/" + denom
private lazy val g = {
require(denomArg != 0)
gcd(numerArg, denomArg)
}
private def gcd(a: Int, b: Int): Int =
if (b == 0) a else gcd(b, a % b)
}
Listing 20.8 · Initializing a trait with lazy vals.
Here’s an example:
scala> val x = 2
x: Int = 2
scala> new LazyRationalTrait {
val numerArg = 1 * x
val denomArg = 2 * x
}
res7: LazyRationalTrait = 1/2
No pre-initialization is needed. It’s instructive to trace the sequence of
initializations that lead to the string 1/2 to be printed in the code above:
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1. A fresh instance of LazyRationalTrait gets created and the initialization code of LazyRationalTrait is run. This initialization code is
empty; none of the fields of LazyRationalTrait is initialized yet.
2. Next, the primary constructor of the anonymous subclass defined by
the new expression is executed. This involves the initialization of
numerArg with 2 and denomArg with 4.
3. Next, the toString method is invoked on the constructed object by
the interpreter, so that the resulting value can be printed.
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4. Next, the numer field is accessed for the first time by the toString
method in trait LazyRationalTrait, so its initializer is evaluated.
5. The initializer of numer accesses the private field, g, so g is evaluated
next. This evaluation accesses numerArg and denomArg, which were
defined in Step 2.
6. Next, the toString method accesses the value of denom, which causes
denom’s evaluation. The evaluation of denom accesses the values of
denomArg and g. The initializer of the g field is not re-evaluated, because it was already evaluated in Step 5.
7. Finally, the result string "1/2" is constructed and printed.
Note that the definition of g comes textually after the definitions of numer
and denom in class LazyRationalTrait. Nevertheless, because all three
values are lazy, g gets initialized before the initialization of numer and denom
is completed.
This shows an important property of lazy vals: The textual order of their
definitions does not matter because values get initialized on demand. Thus,
lazy vals can free you as a programmer from having to think hard how to
arrange val definitions to ensure that everything is defined when it is needed.
However, this advantage holds only as long as the initialization of lazy
vals neither produces side effects nor depends on them. In the presence of
side effects, initialization order starts to matter. And then it can be quite
difficult to trace in what order initialization code is run, as the previous example has demonstrated. So lazy vals are an ideal complement to functional
objects, where the order of initializations does not matter, as long as everything gets initialized eventually. They are less well suited for code that’s
predominantly imperative.
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Lazy functional languages
Scala is by no means the first language to have exploited the perfect
match of lazy definitions and functional code. In fact, there is a category of “lazy functional programming languages” in which every value
and parameter is initialized lazily. The best known member of this class
of languages is Haskell [SPJ02].
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20.6
Chapter 20 · Abstract Members
Abstract types
In the beginning of this chapter, you saw, “type T”, an abstract type declaration. The rest of this chapter discusses what such an abstract type declaration means and what it’s good for. Like all other abstract declarations, an
abstract type declaration is a placeholder for something that will be defined
concretely in subclasses. In this case, it is a type that will be defined further
down the class hierarchy. So T above refers to a type that is as yet unknown
at the point where it is declared. Different subclasses can provide different
realizations of T.
Here is a well-known example where abstract types show up naturally.
Suppose you are given the task of modeling the eating habits of animals. You
might start with a class Food and a class Animal with an eat method:
class Food
abstract class Animal {
def eat(food: Food)
}
You might then attempt to specialize these two classes to a class of Cows that
eat Grass:
class Grass extends Food
class Cow extends Animal {
override def eat(food: Grass) = {} // This won't compile
}
However, if you tried to compile the new classes, you’d get the following
compilation errors:
BuggyAnimals.scala:7: error: class Cow needs to be
abstract, since method eat in class Animal of type
(Food)Unit is not defined
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class Cow extends Animal {
ˆ
BuggyAnimals.scala:8: error: method eat overrides nothing
override def eat(food: Grass) = {}
ˆ
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What happened is that the eat method in class Cow did not override the eat
method in class Animal because its parameter type is different: it’s Grass in
class Cow vs. Food in class Animal.
Some people have argued that the type system is unnecessarily strict in
refusing these classes. They have said that it should be OK to specialize a
parameter of a method in a subclass. However, if the classes were allowed
as written, you could get yourself in unsafe situations very quickly.
For instance, the following script would pass the type checker:
class Food
abstract class Animal {
def eat(food: Food)
}
class Grass extends Food
class Cow extends Animal {
override def eat(food: Grass) = {} // This won't compile,
}
// but if it did,...
class Fish extends Food
val bessy: Animal = new Cow
bessy eat (new Fish)
// ...you could feed fish to cows.
The program would compile if the restriction were eased, because Cows are
Animals and Animals do have an eat method that accepts any kind of Food,
including Fish. But surely it would do a cow no good to eat a fish!
What you need to do instead is apply some more precise modeling.
Animals do eat Food, but what kind of Food each Animal eats depends on
the Animal. This can be neatly expressed with an abstract type, as shown in
Listing 20.9:
class Food
abstract class Animal {
type SuitableFood <: Food
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def eat(food: SuitableFood)
}
Listing 20.9 · Modeling suitable food with an abstract type.
With the new class definition, an Animal can eat only food that’s suitable.
What food is suitable cannot be determined at the level of the Animal class.
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That’s why SuitableFood is modeled as an abstract type. The type has an
upper bound, Food, which is expressed by the “<: Food” clause. This means
that any concrete instantiation of SuitableFood (in a subclass of Animal)
must be a subclass of Food. For example, you would not be able to instantiate
SuitableFood with class IOException.
With Animal defined, you can now progress to cows, as shown in Listing 20.10. Class Cow fixes its SuitableFood to be Grass and also defines a
concrete eat method for this kind of food.
class Grass extends Food
class Cow extends Animal {
type SuitableFood = Grass
override def eat(food: Grass) = {}
}
Listing 20.10 · Implementing an abstract type in a subclass.
These new class definitions compile without errors. If you tried to run
the “cows-that-eat-fish” counterexample with the new class definitions, you
would get the following compiler error:
scala> class Fish extends Food
defined class Fish
scala> val bessy: Animal = new Cow
bessy: Animal = Cow@1515d8a6
scala> bessy eat (new Fish)
:14: error: type mismatch;
found
: Fish
required: bessy.SuitableFood
bessy eat (new Fish)
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ˆ
20.7
Path-dependent types
Have a look at the last error message again. What’s interesting about it is the
type required by the eat method: bessy.SuitableFood. This type consists
of an object reference, bessy, followed by a type field, SuitableFood, of
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the object. So this shows that objects in Scala can have types as members.
The meaning of bessy.SuitableFood is “the type SuitableFood that is a
member of the object referenced from bessy” or, alternatively, the type of
food that’s suitable for bessy.
A type like bessy.SuitableFood is called a path-dependent type. The
word “path” here means a reference an object. It could be a single name,
such as bessy, or a longer access path, such as farm.barn.bessy, where
each of farm, barn, and bessy are variables (or singleton object names) that
refer to objects.
As the term “path-dependent type” implies, the type depends on the path;
in general, different paths give rise to different types. For instance, say you
defined classes DogFood and Dog, like this:
class DogFood extends Food
class Dog extends Animal {
type SuitableFood = DogFood
override def eat(food: DogFood) = {}
}
If you attempted to feed a dog with food fit for a cow, your code would
not compile:
scala> val bessy = new Cow
bessy: Cow = Cow@713e7e09
scala> val lassie = new Dog
lassie: Dog = Dog@6eaf2c57
scala> lassie eat (new bessy.SuitableFood)
:16: error: type mismatch;
found
: Grass
required: DogFood
lassie eat (new bessy.SuitableFood)
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ˆ
The problem here is that the type of the SuitableFood object passed to the
eat method, bessy.SuitableFood, is incompatible with the parameter type
of eat, lassie.SuitableFood.
The case would be different for two Dogs. Because Dog’s SuitableFood
type is defined to be an alias for class DogFood, the SuitableFood types of
two Dogs are in fact the same. As a result, the Dog instance named lassie
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could actually eat the suitable food of a different Dog instance (which we’ll
name bootsie):
scala> val bootsie = new Dog
bootsie: Dog = Dog@13a7c48c
scala> lassie eat (new bootsie.SuitableFood)
A path-dependent type resembles the syntax for an inner class type in
Java, but there is a crucial difference: a path-dependent type names an outer
object, whereas an inner class type names an outer class. Java-style inner
class types can also be expressed in Scala, but they are written differently.
Consider these two classes, Outer and Inner:
class Outer {
class Inner
}
In Scala, the inner class is addressed using the expression Outer#Inner instead of Java’s Outer.Inner. The ‘.’ syntax is reserved for objects. For
example, imagine you instantiate two objects of type Outer, like this:
val o1 = new Outer
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val o2 = new Outer
Here o1.Inner and o2.Inner are two path-dependent types (and they are
different types). Both of these types conform to (are subtypes of) the more
general type Outer#Inner, which represents the Inner class with an arbitrary outer object of type Outer. By contrast, type o1.Inner refers to the
Inner class with a specific outer object (the one referenced from o1). Likewise, type o2.Inner refers to the Inner class with a different, specific outer
object (the one referenced from o2).
In Scala, as in Java, inner class instances hold a reference to an enclosing outer class instance. This allows an inner class, for example, to access
members of its outer class. Thus you can’t instantiate an inner class without
in some way specifying an outer class instance. One way to do this is to
instantiate the inner class inside the body of the outer class. In this case, the
current outer class instance (referenced from this) will be used.
Another way is to use a path-dependent type. For example, because the
type, o1.Inner, names a specific outer object, you can instantiate it:
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scala> new o1.Inner
res11: o1.Inner = Outer$Inner@1ae1e03f
The resulting inner object will contain a reference to its outer object, the
object referenced from o1. By contrast, because the type Outer#Inner does
not name any specific instance of Outer, you can’t create an instance of it:
scala> new Outer#Inner
:9: error: Outer is not a legal prefix for a
constructor
new Outer#Inner
ˆ
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20.8
Refinement types
When a class inherits from another, the first class is said to be a nominal
subtype of the other one. It’s a nominal subtype because each type has a
name, and the names are explicitly declared to have a subtyping relationship.
Scala additionally supports structural subtyping, where you get a subtyping
relationship simply because two types have compatible members. To get
structural subtyping in Scala, use Scala’s refinement types.
Nominal subtyping is usually more convenient, so you should try nominal types first with any new design. A name is a single short identifier
and thus is more concise than an explicit listing of member types. Further, structural subtyping is often more flexible than you want. A widget
can draw(), and a Western cowboy can draw(), but they aren’t really substitutable. You’d typically prefer to get a compilation error if you tried to
substitute a cowboy for a widget.
Nonetheless, structural subtyping has its own advantages. One is that
sometimes there really is no more to a type than its members. For example,
suppose you want to define a Pasture class that can contain animals that
eat grass. One option would be to define a trait AnimalThatEatsGrass and
mix it into every class where it applies. It would be verbose, however. Class
Cow has already declared that it’s an animal and that it eats grass, and now it
would have to declare that it is also an animal-that-eats-grass.
Instead of defining AnimalThatEatsGrass, you can use a refinement
type. Simply write the base type, Animal, followed by a sequence of members listed in curly braces. The members in the curly braces further specify—
or refine, if you will—the types of members from the base class.
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Here is how you write the type, “animal that eats grass”:
Animal { type SuitableFood = Grass }
Given this type, you can now write the pasture class like this:
class Pasture {
var animals: List[Animal { type SuitableFood = Grass }] = Nil
// ...
}
20.9
Enumerations
An interesting application of path-dependent types is found in Scala’s support for enumerations. Some other languages, including Java and C#, have
enumerations as a built-in language construct to define new types. Scala
does not need special syntax for enumerations. Instead, there’s a class in its
standard library, scala.Enumeration.
To create a new enumeration, you define an object that extends this class,
as in the following example, which defines a new enumeration of Colors:
object Color extends Enumeration {
val Red = Value
val Green = Value
val Blue = Value
}
Scala lets you also shorten several successive val or var definitions with the
same right-hand side. Equivalently to the above you could write:
object Color extends Enumeration {
val Red, Green, Blue = Value
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}
This object definition provides three values: Color.Red, Color.Green, and
Color.Blue. You could also import everything in Color with:
import Color._
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and then just use Red, Green, and Blue. But what is the type of these values?
Enumeration defines an inner class named Value, and the same-named
parameterless Value method returns a fresh instance of that class. In other
words, a value such as Color.Red is of type Color.Value; Color.Value
is the type of all enumeration values defined in object Color. It’s a pathdependent type, with Color being the path and Value being the dependent
type. What’s significant about this is that it is a completely new type, different from all other types.
In particular, if you define another enumeration, such as:
object Direction extends Enumeration {
val North, East, South, West = Value
}
then Direction.Value would be different from Color.Value because the
path parts of the two types differ.
Scala’s Enumeration class also offers many other features found in the
enumeration designs of other languages. You can associate names with enumeration values by using a different overloaded variant of the Value method:
object Direction extends Enumeration {
val North = Value("North")
val East = Value("East")
val South = Value("South")
val West = Value("West")
}
You can iterate over the values of an enumeration via the set returned by the
enumeration’s values method:
scala> for (d <- Direction.values) print(d + " ")
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North East South West
Values of an enumeration are numbered from 0, and you can find out the
number of an enumeration value by its id method:
scala> Direction.East.id
res14: Int = 1
It’s also possible to go the other way, from a non-negative integer number to
the value that has this number as id in an enumeration:
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scala> Direction(1)
res15: Direction.Value = East
This should be enough to get you started with enumerations. You can find
more information in the Scaladoc comments of class scala.Enumeration.
20.10
Case study: Currencies
The rest of this chapter presents a case study that explains how abstract types
can be used in Scala. The task is to design a class Currency. A typical
instance of Currency would represent an amount of money in dollars, euros,
yen, or some other currency. It should be possible to do some arithmetic on
currencies. For instance, you should be able to add two amounts of the same
currency. Or you should be able to multiply a currency amount by a factor
representing an interest rate.
These thoughts lead to the following first design for a currency class:
// A first (faulty) design of the Currency class
abstract class Currency {
val amount: Long
def designation: String
override def toString = amount + " " + designation
def + (that: Currency): Currency = ...
def * (x: Double): Currency = ...
}
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The amount of a currency is the number of currency units it represents. This
is a field of type Long so that very large amounts of money, such as the market capitalization of Google or Apple, can be represented. It’s left abstract
here, waiting to be defined when a subclass talks about concrete amounts of
money. The designation of a currency is a string that identifies it. The
toString method of class Currency indicates an amount and a designation.
It would yield results such as:
79 USD
11000 Yen
99 Euro
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Finally, there are methods + for adding currencies and * for multiplying a
currency with a floating-point number. You can create a concrete currency
value by supplying concrete amount and designation values, like this:
new Currency {
val amount = 79L
def designation = "USD"
}
This design would be OK if all we wanted to model was a single currency,
like only dollars or only euros. But it fails if we need to deal with several
currencies. Assume that you model dollars and euros as two subclasses of
class currency:
abstract class Dollar extends Currency {
def designation = "USD"
}
abstract class Euro extends Currency {
def designation = "Euro"
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}
At first glance this looks reasonable. But it would let you add dollars to
euros. The result of such an addition would be of type Currency. But it
would be a funny currency that was made up of a mix of euros and dollars.
What you want instead is a more specialized version of the + method. When
implemented in class Dollar, it should take Dollar arguments and yield a
Dollar result; when implemented in class Euro, it should take Euro arguments and yield a Euro result. So the type of the addition method would
change depending on which class you are in. Nonetheless, you would like to
write the addition method just once, not each time a new currency is defined.
In Scala, there’s a simple technique to deal with situations like this. If
something is not known at the point where a class is defined, make it abstract
in the class. This applies to both values and types. In the case of currencies,
the exact argument and result type of the addition method are not known, so
it is a good candidate for an abstract type.
This would lead to the following sketch of class AbstractCurrency:
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// A second (still imperfect) design of the Currency class
abstract class AbstractCurrency {
type Currency <: AbstractCurrency
val amount: Long
def designation: String
override def toString = amount + " " + designation
def + (that: Currency): Currency = ...
def * (x: Double): Currency = ...
}
The only differences from the previous situation are that the class is now
called AbstractCurrency, and that it contains an abstract type Currency,
which represents the real currency in question. Each concrete subclass of
AbstractCurrency would need to fix the Currency type to refer to the
concrete subclass itself, thereby “tying the knot.”
For instance, here is a new version of class Dollar, which now extends
class AbstractCurrency:
abstract class Dollar extends AbstractCurrency {
type Currency = Dollar
def designation = "USD"
}
This design is workable, but it is still not perfect. One problem is hidden by
the ellipses that indicate the missing method definitions of + and * in class
AbstractCurrency. In particular, how should addition be implemented
in this class? It’s easy enough to calculate the correct amount of the new
currency as this.amount + that.amount, but how would you convert the
amount into a currency of the right type?
You might try something like:
def + (that: Currency): Currency = new Currency {
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val amount = this.amount + that.amount
}
However, this would not compile:
error: class type required
def + (that: Currency): Currency = new Currency {
ˆ
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One of the restrictions of Scala’s treatment of abstract types is that you can
neither create an instance of an abstract type nor have an abstract type as a
supertype of another class.1 So the compiler would refuse the example code
here that attempted to instantiate Currency.
However, you can work around this restriction using a factory method.
Instead of creating an instance of an abstract type directly, declare an abstract
method that does it. Then, wherever the abstract type is fixed to be some
concrete type, you also need to give a concrete implementation of the factory
method. For class AbstractCurrency, this would look as follows:
abstract class AbstractCurrency {
type Currency <: AbstractCurrency // abstract type
def make(amount: Long): Currency
// factory method
...
// rest of class
}
A design like this could be made to work, but it looks rather suspicious.
Why place the factory method inside class AbstractCurrency? This looks
dubious for at least two reasons. First, if you have some amount of currency
(say, one dollar), you also hold in your hand the ability to make more of the
same currency, using code such as:
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myDollar.make(100)
// here are a hundred more!
In the age of color copying this might be a tempting scenario, but hopefully
not one which you would be able to do for very long without being caught.
The second problem with this code is that you can make more Currency
objects if you already have a reference to a Currency object. But how do
you get the first object of a given Currency? You’d need another creation
method, which does essentially the same job as make. So you have a case of
code duplication, which is a sure sign of a code smell.
The solution, of course, is to move the abstract type and the factory
method outside class AbstractCurrency. You need to create another class
that contains the AbstractCurrency class, the Currency type, and the make
factory method.
We’ll call this a CurrencyZone:
1 There’s some promising recent research on virtual classes, which would allow this, but
virtual classes are not currently supported in Scala.
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abstract class CurrencyZone {
type Currency <: AbstractCurrency
def make(x: Long): Currency
abstract class AbstractCurrency {
val amount: Long
def designation: String
override def toString = amount + " " + designation
def + (that: Currency): Currency =
make(this.amount + that.amount)
def * (x: Double): Currency =
make((this.amount * x).toLong)
}
}
An example concrete CurrencyZone is the US, which could be defined as:
object US extends CurrencyZone {
abstract class Dollar extends AbstractCurrency {
def designation = "USD"
}
type Currency = Dollar
def make(x: Long) = new Dollar { val amount = x }
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}
Here, US is an object that extends CurrencyZone. It defines a class Dollar,
which is a subclass of AbstractCurrency. So the type of money in this
zone is US.Dollar. The US object also fixes the type Currency to be an
alias for Dollar, and it gives an implementation of the make factory method
to return a dollar amount.
This is a workable design. There are only a few refinements to be added.
The first refinement concerns subunits. So far, every currency was measured in a single unit: dollars, euros, or yen. However, most currencies have
subunits: For instance, in the US, it’s dollars and cents. The most straightforward way to model cents is to have the amount field in US.Currency
represent cents instead of dollars. To convert back to dollars, it’s useful to
introduce a field CurrencyUnit into class CurrencyZone, which contains
the amount of one standard unit in that currency:
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class CurrencyZone {
...
val CurrencyUnit: Currency
}
As shown in Listing 20.11, The US object could define the quantities
Cent, Dollar, and CurrencyUnit. This definition is just like the previous definition of the US object, except that it adds three new fields. The
field Cent represents an amount of 1 US.Currency. It’s an object analogous to a one-cent coin. The field Dollar represents an amount of 100
US.Currency. So the US object now defines the name Dollar in two ways.
The type Dollar (defined by the abstract inner class named Dollar) represents the generic name of the Currency valid in the US currency zone. By
contrast, the value Dollar (referenced from the val field named Dollar)
represents a single US dollar, analogous to a one-dollar bill. The third field
definition of CurrencyUnit specifies that the standard currency unit in the
US zone is the Dollar (i.e., the value Dollar, referenced from the field, not
the type Dollar).
The toString method in class Currency also needs to be adapted to
take subunits into account. For instance, the sum of ten dollars and twenty
three cents should print as a decimal number: 10.23 USD. To achieve this,
you could implement Currency’s toString method as follows:
override def toString =
((amount.toDouble / CurrencyUnit.amount.toDouble)
formatted ("%." + decimals(CurrencyUnit.amount) + "f")
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+ " " + designation)
Here, formatted is a method that Scala makes available on several
classes, including Double.2 The formatted method returns the string that
results from formatting the original string on which formatted was invoked
according to a format string passed as the formatted method’s right-hand
operand. The syntax of format strings passed to formatted is the same as
that of Java’s String.format method.
For instance, the format string %.2f formats a number with two decimal digits. The format string used in the toString shown previously is assembled by calling the decimals method on CurrencyUnit.amount. This
2 Scala
uses rich wrappers, described in Section 5.10, to make formatted available.
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Section 20.10
Chapter 20 · Abstract Members
object US extends CurrencyZone {
abstract class Dollar extends AbstractCurrency {
def designation = "USD"
}
type Currency = Dollar
def make(cents: Long) = new Dollar {
val amount = cents
}
val Cent = make(1)
val Dollar = make(100)
val CurrencyUnit = Dollar
}
Listing 20.11 · The US currency zone.
method returns the number of decimal digits of a decimal power minus one.
For instance, decimals(10) is 1, decimals(100) is 2, and so on. The
decimals method is implemented by a simple recursion:
private def decimals(n: Long): Int =
if (n == 1) 0 else 1 + decimals(n / 10)
Listing 20.12 shows some other currency zones. As another refinement,
you can add a currency conversion feature to the model. First, you could
write a Converter object that contains applicable exchange rates between
currencies, as shown in Listing 20.13. Then, you could add a conversion
method, from, to class Currency, which converts from a given source currency into the current Currency object:
def from(other: CurrencyZone#AbstractCurrency): Currency =
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make(math.round(
other.amount.toDouble * Converter.exchangeRate
(other.designation)(this.designation)))
The from method takes an arbitrary currency as argument. This is expressed
by its formal parameter type, CurrencyZone#AbstractCurrency, which
indicates that the argument passed as other must be an AbstractCurrency
type in some arbitrary and unknown CurrencyZone. It produces its result
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object Europe extends CurrencyZone {
abstract class Euro extends AbstractCurrency {
def designation = "EUR"
}
type Currency = Euro
def make(cents: Long) = new Euro {
val amount = cents
}
val Cent = make(1)
val Euro = make(100)
val CurrencyUnit = Euro
}
object Japan extends CurrencyZone {
abstract class Yen extends AbstractCurrency {
def designation = "JPY"
}
type Currency = Yen
def make(yen: Long) = new Yen {
val amount = yen
}
val Yen = make(1)
val CurrencyUnit = Yen
}
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Listing 20.12 · Currency zones for Europe and Japan.
by multiplying the amount of the other currency with the exchange rate
between the other and the current currency.3
The final version of the CurrencyZone class is shown in Listing 20.14.
You can test the class in the Scala command shell. We’ll assume that the
CurrencyZone class and all concrete CurrencyZone objects are defined
in a package org.stairwaybook.currencies. The first step is to import
“org.stairwaybook.currencies._” into the command shell. Then you
can do some currency conversions:
3 By
the way, in case you think you’re getting a bad deal on Japanese yen, the exchange
rates convert currencies based on their CurrencyZone amounts. Thus, 1.211 is the exchange
rate between US cents and Japanese yen.
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471
object Converter {
var exchangeRate = Map(
"USD" -> Map("USD" -> 1.0
, "EUR" -> 0.7596,
"JPY" -> 1.211 , "CHF" -> 1.223),
"EUR" -> Map("USD" -> 1.316 , "EUR" -> 1.0
,
"JPY" -> 1.594 , "CHF" -> 1.623),
"JPY" -> Map("USD" -> 0.8257, "EUR" -> 0.6272,
"JPY" -> 1.0
, "CHF" -> 1.018),
"CHF" -> Map("USD" -> 0.8108, "EUR" -> 0.6160,
"JPY" -> 0.982 , "CHF" -> 1.0
)
)
}
Listing 20.13 · A converter object with an exchange rates map.
scala> Japan.Yen from US.Dollar * 100
res16: Japan.Currency = 12110 JPY
scala> Europe.Euro from res16
res17: Europe.Currency = 75.95 EUR
scala> US.Dollar from res17
res18: US.Currency = 99.95 USD
The fact that we obtain almost the same amount after three conversions
implies that these are some pretty good exchange rates! You can also add up
values of the same currency:
scala> US.Dollar * 100 + res18
res19: US.Currency = 199.95 USD
On the other hand, you cannot add amounts of different currencies:
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scala> US.Dollar + Europe.Euro
:12: error: type mismatch;
found
: Europe.Euro
required: US.Currency
(which expands to)
US.Dollar
US.Dollar + Europe.Euro
ˆ
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Section 20.10
Chapter 20 · Abstract Members
abstract class CurrencyZone {
type Currency <: AbstractCurrency
def make(x: Long): Currency
abstract class AbstractCurrency {
val amount: Long
def designation: String
def + (that: Currency): Currency =
make(this.amount + that.amount)
def * (x: Double): Currency =
make((this.amount * x).toLong)
def - (that: Currency): Currency =
make(this.amount - that.amount)
def / (that: Double) =
make((this.amount / that).toLong)
def / (that: Currency) =
this.amount.toDouble / that.amount
def from(other: CurrencyZone#AbstractCurrency): Currency =
make(math.round(
other.amount.toDouble * Converter.exchangeRate
(other.designation)(this.designation)))
private def decimals(n: Long): Int =
if (n == 1) 0 else 1 + decimals(n / 10)
override def toString =
((amount.toDouble / CurrencyUnit.amount.toDouble)
formatted ("%." + decimals(CurrencyUnit.amount) + "f")
+ " " + designation)
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}
val CurrencyUnit: Currency
}
Listing 20.14 · The full code of class CurrencyZone.
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472
Section 20.11
Chapter 20 · Abstract Members
By preventing the addition of two values with different units (in this case,
currencies), the type abstraction has done its job. It prevents us from performing calculations that are unsound. Failures to convert correctly between
different units may seem like trivial bugs, but they have caused many serious systems faults. An example is the crash of the Mars Climate Orbiter
spacecraft on September 23, 1999, which was caused because one engineering team used metric units while another used English units. If units had
been coded in the same way as currencies are coded in this chapter, this error
would have been detected by a simple compilation run. Instead, it caused the
crash of the orbiter after a near ten-month voyage.
20.11
Conclusion
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Scala offers systematic and very general support for object-oriented abstraction. It enables you to not only abstract over methods, but also over values,
variables, and types. This chapter has shown how to take advantage of abstract members. They support a simple yet effective principle for systems
structuring: when designing a class, make everything that is not yet known
into an abstract member. The type system will then drive the development of
your model, just as you saw with the currency case study. It does not matter
whether the unknown is a type, method, variable or value. In Scala, all of
these can be declared abstract.
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473
Chapter 21
Implicit Conversions and Parameters
There’s a fundamental difference between your own code and other people’s
libraries: You can change or extend your own code as you wish, but if you
want to use someone else’s libraries, you usually have to take them as they
are. A number of constructs have sprung up in programming languages to
alleviate this problem. Ruby has modules, and Smalltalk lets packages add
to each other’s classes. These are very powerful but also dangerous, in that
you can modify the behavior of a class for an entire application, some parts
of which you might not know. C# 3.0 has static extension methods, which
are more local but also more restrictive, in that you can only add methods,
not fields, to a class, and you can’t make a class implement new interfaces.
Scala’s answer is implicit conversions and parameters. These can make
existing libraries much more pleasant to deal with by letting you leave out
tedious, obvious details that obscure the interesting parts of your code. Used
tastefully, this results in code that is focused on the interesting, non-trivial
parts of your program. This chapter shows you how implicits work, and it
presents some of the most common ways they are used.
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21.1
Implicit conversions
Before delving into the details of implicit conversions, take a look at a typical
example of their use. Implicit conversions are often helpful for working with
two bodies of software that were developed without each other in mind. Each
library has its own way to encode a concept that is essentially the same thing.
Implicit conversions help by reducing the number of explicit conversions that
are needed from one type to another.
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Section 21.1
Chapter 21 · Implicit Conversions and Parameters
Java includes a library named Swing for implementing cross-platform
user interfaces. One of the things Swing does is process events from the
operating system, convert them to platform-independent event objects, and
pass those events to parts of an application called event listeners.
If Swing had been written with Scala in mind, event listeners would probably have been represented by a function type. Callers could then use the
function literal syntax as a lightweight way to specify what should happen
for a certain class of events. Since Java doesn’t have function literals, Swing
uses the next best thing, an inner class that implements a one-method interface. In the case of action listeners, the interface is ActionListener.
Without the use of implicit conversions, a Scala program that uses Swing
must use inner classes just like in Java. Here’s an example that creates a button and hooks up an action listener to it. The action listener is invoked whenever the button is pressed, at which point it prints the string "pressed!":
val button = new JButton
button.addActionListener(
new ActionListener {
def actionPerformed(event: ActionEvent) = {
println("pressed!")
}
}
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)
This code has a lot of information-free boilerplate. The fact that this listener is an ActionListener, the fact that the callback method is named
actionPerformed, and the fact that the argument is an ActionEvent are
all implied for any argument to addActionListener. The only new information here is the code to be performed, namely the call to println. This
new information is drowned out by the boilerplate. Someone reading this
code will need to have an eagle’s eye to pick through the noise and find the
informative part.
A more Scala-friendly version would take a function as an argument,
greatly reducing the amount of boilerplate:
button.addActionListener( // Type mismatch!
(_: ActionEvent) => println("pressed!")
)
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As written so far, this code doesn’t work.1 The addActionListener method
wants an action listener but is getting a function. With implicit conversions,
however, this code can be made to work.
The first step is to write an implicit conversion between the two types.
Here is an implicit conversion from functions to action listeners:
implicit def function2ActionListener(f: ActionEvent => Unit) =
new ActionListener {
def actionPerformed(event: ActionEvent) = f(event)
}
This is a one-argument method that takes a function and returns an action
listener. Like any other one-argument method, it can be called directly and
have its result passed on to another expression:
button.addActionListener(
function2ActionListener(
(_: ActionEvent) => println("pressed!")
)
)
This is already an improvement on the version with the inner class. Note how
arbitrary amounts of boilerplate end up replaced by a function literal and a
call to a method. It gets better, though, with implicit conversions. Because
function2ActionListener is marked as implicit, it can be left out and the
compiler will insert it automatically. Here is the result:
// Now this works
button.addActionListener(
(_: ActionEvent) => println("pressed!")
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)
The way this code works is that the compiler first tries to compile it as is, but
it sees a type error. Before giving up, it looks for an implicit conversion that
can repair the problem. In this case, it finds function2ActionListener. It
tries that conversion method, sees that it works, and moves on. The compiler
works hard here so that the developer can ignore one more fiddly detail.
1 As
will be explained in Section 31.5, it does work in Scala 2.12.
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Action listener? Action event function? Either one will work—use the one
that’s more convenient.
In this section, we illustrated some some of the power of implicit conversions and how they let you dress up existing libraries. In the next sections,
you’ll learn the rules that determine when implicit conversions are tried and
how they are found.
21.2
Rules for implicits
Implicit definitions are those that the compiler is allowed to insert into a
program in order to fix any of its type errors. For example, if x + y does
not type check, then the compiler might change it to convert(x) + y, where
convert is some available implicit conversion. If convert changes x into
something that has a + method, then this change might fix a program so that it
type checks and runs correctly. If convert really is just a simple conversion
function, then leaving it out of the source code can be a clarification.
Implicit conversions are governed by the following general rules:
Marking rule: Only definitions marked implicit are available. The
implicit keyword is used to mark which declarations the compiler may
use as implicits. You can use it to mark any variable, function, or object
definition. Here’s an example of an implicit function definition:2
implicit def intToString(x: Int) = x.toString
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The compiler will only change x + y to convert(x) + y if convert is marked
as implicit. This way, you avoid the confusion that would result if the
compiler picked random functions that happen to be in scope and inserted
them as “conversions.” The compiler will only select among the definitions
you have explicitly marked as implicit.
Scope rule: An inserted implicit conversion must be in scope as a single
identifier, or be associated with the source or target type of the conversion. The Scala compiler will only consider implicit conversions that are
in scope. To make an implicit conversion available, therefore, you must in
2 Variables
and singleton objects marked implicit can be used as implicit parameters.
This use case will be described later in this chapter.
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Section 21.2
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some way bring it into scope. Moreover, with one exception, the implicit
conversion must be in scope as a single identifier. The compiler will not
insert a conversion of the form someVariable.convert. For example, it
will not expand x + y to someVariable.convert(x) + y. If you want to
make someVariable.convert available as an implicit, you would need to
import it, which would make it available as a single identifier. Once imported, the compiler would be free to apply it as convert(x) + y. In fact, it
is common for libraries to include a Preamble object including a number of
useful implicit conversions. Code that uses the library can then do a single
“import Preamble._” to access the library’s implicit conversions.
There’s one exception to the “single identifier” rule. The compiler will
also look for implicit definitions in the companion object of the source or
expected target types of the conversion. For example, if you’re attempting
to pass a Dollar object to a method that takes a Euro, the source type is
Dollar and the target type is Euro. You could, therefore, package an implicit
conversion from Dollar to Euro in the companion object of either class,
Dollar or Euro.
Here’s an example in which the implicit definition is placed in Dollar’s
companion object:
object Dollar {
implicit def dollarToEuro(x: Dollar): Euro = ...
}
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class Dollar { ... }
In this case, the conversion dollarToEuro is said to be associated to the
type Dollar. The compiler will find such an associated conversion every
time it needs to convert from an instance of type Dollar. There’s no need to
import the conversion separately into your program.
The Scope Rule helps with modular reasoning. When you read code in
a file, the only things you need to consider from other files are those that are
either imported or are explicitly referenced through a fully qualified name.
This benefit is at least as important for implicits as for explicitly written code.
If implicits took effect system-wide, then to understand a file you would have
to know about every implicit introduced anywhere in the program!
One-at-a-time rule: Only one implicit is inserted. The compiler will
never rewrite x + y to convert1(convert2(x)) + y. Doing so would cause
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compile times to increase dramatically on erroneous code, and it would increase the difference between what the programmer writes and what the program actually does. For sanity’s sake, the compiler does not insert further implicit conversions when it is already in the middle of trying another implicit.
However, it’s possible to circumvent this restriction by having implicits take
implicit parameters, which will be described later in this chapter.
Explicits-first rule: Whenever code type checks as it is written, no implicits are attempted. The compiler will not change code that already
works. A corollary of this rule is that you can always replace implicit identifiers by explicit ones, thus making the code longer but with less apparent
ambiguity. You can trade between these choices on a case-by-case basis.
Whenever you see code that seems repetitive and verbose, implicit conversions can help you decrease the tedium. Whenever code seems terse to the
point of obscurity, you can insert conversions explicitly. The amount of implicits you leave the compiler to insert is ultimately a matter of style.
Naming an implicit conversion
Implicit conversions can have arbitrary names. The name of an implicit conversion matters only in two situations: If you want to write it explicitly in a
method application and for determining which implicit conversions are available at any place in the program. To illustrate the second point, say you have
an object with two implicit conversions:
object MyConversions {
implicit def stringWrapper(s: String):
IndexedSeq[Char] = ...
implicit def intToString(x: Int): String = ...
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}
In your application, you want to make use of the stringWrapper conversion, but you don’t want integers to be converted automatically to strings by
means of the intToString conversion. You can achieve this by importing
only one conversion, but not the other:
import MyConversions.stringWrapper
... // code making use of stringWrapper
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In this example, it was important that the implicit conversions had names,
because only that way could you selectively import one and not the other.
Where implicits are tried
There are three places implicits are used in the language: conversions to an
expected type, conversions of the receiver of a selection, and implicit parameters. Implicit conversions to an expected type let you use one type in a
context where a different type is expected. For example, you might have a
String and want to pass it to a method that requires an IndexedSeq[Char].
Conversions of the receiver let you adapt the receiver of a method call (i.e.,
the object on which a method is invoked), if the method is not applicable
on the original type. An example is "abc".exists, which is converted to
stringWrapper("abc").exists because the exists method is not available on Strings but is available on IndexedSeqs. Implicit parameters, on
the other hand, are usually used to provide more information to the called
function about what the caller wants. Implicit parameters are especially useful with generic functions, where the called function might otherwise know
nothing at all about the type of one or more arguments. We will examine
each of these three kinds of implicits in the next sections.
21.3
Implicit conversion to an expected type
Implicit conversion to an expected type is the first place the compiler will use
implicits. The rule is simple. Whenever the compiler sees an X, but needs
a Y, it will look for an implicit function that converts X to Y. For example,
normally a double cannot be used as an integer because it loses precision:
scala> val i: Int = 3.5
:7: error: type mismatch;
found
: Double(3.5)
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required: Int
val i: Int = 3.5
ˆ
However, you can define an implicit conversion to smooth this over:
scala> implicit def doubleToInt(x: Double) = x.toInt
doubleToInt: (x: Double)Int
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scala> val i: Int = 3.5
i: Int = 3
What happens here is that the compiler sees a Double, specifically 3.5, in
a context where it requires an Int. So far, the compiler is looking at an
ordinary type error. Before giving up, though, it searches for an implicit
conversion from Double to Int. In this case, it finds one: doubleToInt, because doubleToInt is in scope as a single identifier. (Outside the interpreter,
you might bring doubleToInt into scope via an import or possibly through
inheritance.) The compiler then inserts a call to doubleToInt automatically.
Behind the scenes, the code becomes:
val i: Int = doubleToInt(3.5)
This is literally an implicit conversion. You did not explicitly ask for conversion. Instead, you marked doubleToInt as an available implicit conversion
by bringing it into scope as a single identifier, and then the compiler automatically used it when it needed to convert from a Double to an Int.
Converting Doubles to Ints might raise some eyebrows because, it’s a
dubious idea to have something that causes a loss in precision happen invisibly. So this is not really a conversion we recommend. It makes much
more sense to go the other way, from some more constrained type to a more
general one. For instance, an Int can be converted without loss of precision
to a Double, so an implicit conversion from Int to Double makes sense. In
fact, that’s exactly what happens. The scala.Predef object, which is implicitly imported into every Scala program, defines implicit conversions that
convert “smaller” numeric types to “larger” ones. For instance, you will find
in Predef the following conversion:
implicit def int2double(x: Int): Double = x.toDouble
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That’s why in Scala Int values can be stored in variables of type Double.
There’s no special rule in the type system for this; it’s just an implicit conversion that gets applied.3
3 The Scala compiler backend will treat the conversion specially, however, translating it
to a special “i2d” bytecode. So the compiled image is the same as in Java.
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21.4
Chapter 21 · Implicit Conversions and Parameters
Converting the receiver
Implicit conversions also apply to the receiver of a method call, the object on
which the method is invoked. This kind of implicit conversion has two main
uses. First, receiver conversions allow smoother integration of a new class
into an existing class hierarchy. And second, they support writing domainspecific languages (DSLs) within the language.
To see how it works, suppose you write down obj.doIt, and obj does
not have a member named doIt. The compiler will try to insert conversions
before giving up. In this case, the conversion needs to apply to the receiver,
obj. The compiler will act as if the expected “type” of obj was “has a
member named doIt.” This “has a doIt” type is not a normal Scala type,
but it is there conceptually and is why the compiler will insert an implicit
conversion in this case.
Interoperating with new types
As mentioned previously, one major use of receiver conversions is allowing
smoother integration of new types with existing types. In particular, they
allow you to enable client programmers to use instances of existing types as
if they were instances of your new type. Take, for example, class Rational
shown in Listing 6.5 on page 151. Here’s a snippet of that class again:
class Rational(n: Int, d: Int) {
...
def + (that: Rational): Rational = ...
def + (that: Int): Rational = ...
}
Class Rational has two overloaded variants of the + method, which take
Rationals and Ints, respectively, as arguments. So you can either add two
rational numbers or a rational number and an integer:
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scala> val oneHalf = new Rational(1, 2)
oneHalf: Rational = 1/2
scala> oneHalf + oneHalf
res0: Rational = 1/1
scala> oneHalf + 1
res1: Rational = 3/2
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What about an expression like 1 + oneHalf? This expression is tricky because the receiver, 1, does not have a suitable + method. So the following
gives an error:
scala> 1 + oneHalf
:6: error: overloaded method value + with
alternatives (Double)Double ... cannot be applied
to (Rational)
1 + oneHalf
ˆ
To allow this kind of mixed arithmetic, you need to define an implicit conversion from Int to Rational:
scala> implicit def intToRational(x: Int) =
new Rational(x, 1)
intToRational: (x: Int)Rational
With the conversion in place, converting the receiver does the trick:
scala> 1 + oneHalf
res2: Rational = 3/2
What happens behind the scenes here is that the Scala compiler first tries
to type check the expression 1 + oneHalf as it is. This fails because Int
has several + methods, but none that takes a Rational argument. Next, the
compiler searches for an implicit conversion from Int to another type that
has a + method which can be applied to a Rational. It finds your conversion
and applies it, which yields:
intToRational(1) + oneHalf
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In this case, the compiler found the implicit conversion function because you
entered its definition into the interpreter, which brought it into scope for the
remainder of the interpreter session.
Simulating new syntax
The other major use of implicit conversions is to simulate adding new syntax.
Recall that you can make a Map using syntax like this:
Map(1 -> "one", 2 -> "two", 3 -> "three")
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Have you wondered how the -> is supported? It’s not syntax! Instead, -> is
a method of the class ArrowAssoc, a class defined inside the standard Scala
preamble (scala.Predef). The preamble also defines an implicit conversion from Any to ArrowAssoc. When you write 1 -> "one", the compiler
inserts a conversion from 1 to ArrowAssoc so that the -> method can be
found. Here are the relevant definitions:
package scala
object Predef {
class ArrowAssoc[A](x: A) {
def -> [B](y: B): Tuple2[A, B] = Tuple2(x, y)
}
implicit def any2ArrowAssoc[A](x: A): ArrowAssoc[A] =
new ArrowAssoc(x)
...
}
This “rich wrappers” pattern is common in libraries that provide syntax-like
extensions to the language, so you should be ready to recognize the pattern
when you see it. Whenever you see someone calling methods that appear
not to exist in the receiver class, they are probably using implicits. Similarly,
if you see a class named RichSomething (e.g., RichInt or RichBoolean),
that class is likely adding syntax-like methods to type Something.
You have already seen this rich wrappers pattern for the basic types described in Chapter 5. As you can now see, these rich wrappers apply more
widely, often letting you get by with an internal DSL defined as a library
where programmers in other languages might feel the need to develop an
external DSL.
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Implicit classes
Implicit classes were added in Scala 2.10 to make it easier to write rich wrapper classes. An implicit class is a class that is preceded by the implicit
keyword. For any such class, the compiler generates an implicit conversion
from the class’s constructor parameter to the class itself. Such a conversion
is just what you need if you plan to use the class for the rich wrappers pattern.
For example, suppose you have a class named Rectangle for representing the width and height of a rectangle on the screen:
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case class Rectangle(width: Int, height: Int)
If you use this class very frequently, you might want to use the rich wrappers
pattern so you can more easily construct it. Here’s one way to do so.
implicit class RectangleMaker(width: Int) {
def x(height: Int) = Rectangle(width, height)
}
The above definition defines a RectangleMaker class in the usual manner.
In addition, it causes the following conversion to be automatically generated:
// Automatically generated
implicit def RectangleMaker(width: Int) =
new RectangleMaker(width)
As a result, you can create points by putting an x in between two integers:
scala> val myRectangle = 3 x 4
myRectangle: Rectangle = Rectangle(3,4)
This is how it works: Since type Int has no method named x, the compiler
will look for an implicit conversion from Int to something that does. It will
find the generated RectangleMaker conversion, and RectangleMaker does
have a method named x. The compiler inserts a call to this conversion, after
which the call to x type checks and does what is desired.
As a warning to the adventurous, it might be tempting to think that any
class can have implicit put in front of it. It’s not so. An implicit class
cannot be a case class, and its constructor must have exactly one parameter.
Also, an implicit class must be located within some other object, class, or
trait. In practice, so long as you use implicit classes as rich wrappers to add
a few methods onto an existing class, these restrictions should not matter.
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21.5
Implicit parameters
The remaining place the compiler inserts implicits is within argument lists.
The compiler will sometimes replace someCall(a) with someCall(a)(b),
or new SomeClass(a) with new SomeClass(a)(b), thereby adding a missing parameter list to complete a function call. It is the entire last curried
parameter list that’s supplied, not just the last parameter. For example, if
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someCall’s missing last parameter list takes three parameters, the compiler
might replace someCall(a) with someCall(a)(b, c, d). For this usage,
not only must the inserted identifiers, such as b, c, and d in (b, c, d), be
marked implicit where they are defined, but also the last parameter list in
someCall’s or someClass’s definition must be marked implicit.
Here’s a simple example. Suppose you have a class PreferredPrompt,
which encapsulates a shell prompt string (such as, say "$ " or "> ") that is
preferred by a user:
class PreferredPrompt(val preference: String)
Also, suppose you have a Greeter object with a greet method, which takes
two parameter lists. The first parameter list takes a string user name, and the
second parameter list takes a PreferredPrompt:
object Greeter {
def greet(name: String)(implicit prompt: PreferredPrompt) = {
println("Welcome, " + name + ". The system is ready.")
println(prompt.preference)
}
}
The last parameter list is marked implicit, which means it can be supplied
implicitly. But you can still provide the prompt explicitly, like this:
scala> val bobsPrompt = new PreferredPrompt("relax> ")
bobsPrompt: PreferredPrompt = PreferredPrompt@714d36d6
scala> Greeter.greet("Bob")(bobsPrompt)
Welcome, Bob. The system is ready.
relax>
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To let the compiler supply the parameter implicitly, you must first define
a variable of the expected type, which in this case is PreferredPrompt. You
could do this, for example, in a preferences object:
object JoesPrefs {
implicit val prompt = new PreferredPrompt("Yes, master> ")
}
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Note that the val itself is marked implicit. If it wasn’t, the compiler would
not use it to supply the missing parameter list. It will also not use it if it isn’t
in scope as a single identifier, as shown in this example:
scala> Greeter.greet("Joe")
:13: error: could not find implicit value for
parameter prompt: PreferredPrompt
Greeter.greet("Joe")
ˆ
Once you bring it into scope via an import, however, it will be used to supply
the missing parameter list:
scala> import JoesPrefs._
import JoesPrefs._
scala> Greeter.greet("Joe")
Welcome, Joe. The system is ready.
Yes, master>
Note that the implicit keyword applies to an entire parameter list, not
to individual parameters. Listing 21.1 shows an example in which the last parameter list of Greeter’s greet method, which is again marked implicit,
has two parameters: prompt (of type PreferredPrompt) and drink (of type
PreferredDrink).
Singleton object JoesPrefs declares two implicit vals, prompt of type
PreferredPrompt and drink of type PreferredDrink. As before, however, so long as these are not in scope as single identifiers, they won’t be
used to fill in a missing parameter list to greet:
scala> Greeter.greet("Joe")
:19: error: could not find implicit value for
parameter prompt: PreferredPrompt
Greeter.greet("Joe")
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ˆ
You can bring both implicit vals into scope with an import:
scala> import JoesPrefs._
import JoesPrefs._
Because both prompt and drink are now in scope as single identifiers, you
can use them to supply the last parameter list explicitly, like this:
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class PreferredPrompt(val preference: String)
class PreferredDrink(val preference: String)
object Greeter {
def greet(name: String)(implicit prompt: PreferredPrompt,
drink: PreferredDrink) = {
println("Welcome, " + name + ". The system is ready.")
print("But while you work, ")
println("why not enjoy a cup of " + drink.preference + "?")
println(prompt.preference)
}
}
object JoesPrefs {
implicit val prompt = new PreferredPrompt("Yes, master> ")
implicit val drink = new PreferredDrink("tea")
}
Listing 21.1 · An implicit parameter list with multiple parameters.
scala> Greeter.greet("Joe")(prompt, drink)
Welcome, Joe. The system is ready.
But while you work, why not enjoy a cup of tea?
Yes, master>
And because all the rules for implicit parameters are now met, you can alternatively let the Scala compiler supply prompt and drink for you by leaving
off the last parameter list:
scala> Greeter.greet("Joe")
Welcome, Joe. The system is ready.
But while you work, why not enjoy a cup of tea?
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Yes, master>
One thing to note about the previous examples is that we didn’t use
String as the type of prompt or drink, even though ultimately it was a
String that each of them provided through their preference fields. Because the compiler selects implicit parameters by matching types of parameters against types of values in scope, implicit parameters usually have “rare”
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or “special” enough types that accidental matches are unlikely. For example,
the types PreferredPrompt and PreferredDrink in Listing 21.1 were defined solely to serve as implicit parameter types. As a result, it is unlikely
that implicit variables of these types will be in scope if they aren’t intended
to be used as implicit parameters to Greeter.greet.
Another thing to know about implicit parameters is that they are perhaps
most often used to provide information about a type mentioned explicitly in
an earlier parameter list, similar to the type classes of Haskell.
As an example, consider the maxListOrdering function shown in Listing 21.2, which returns the maximum element of the passed list.
def maxListOrdering[T](elements: List[T])
(ordering: Ordering[T]): T =
elements match {
case List() =>
throw new IllegalArgumentException("empty list!")
case List(x) => x
case x :: rest =>
val maxRest = maxListOrdering(rest)(ordering)
if (ordering.gt(x, maxRest)) x
else maxRest
}
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Listing 21.2 · A function with an upper bound.
The signature of maxListOrdering is similar to that of orderedMergeSort,
shown in Listing 19.12 on page 440: It takes a List[T] as its argument, and
now it takes an additional argument of type Ordering[T]. This additional
argument specifies which ordering to use when comparing elements of type
T. As such, this version can be used for types that don’t have a built-in ordering. Additionally, this version can be used for types that do have a built-in
ordering, but for which you occasionally want to use some other ordering.
This version is more general, but it’s also more cumbersome to use. Now
a caller must specify an explicit ordering even if T is something like String
or Int that has an obvious default ordering. To make the new method more
convenient, it helps to make the second argument implicit. This approach is
shown in Listing 21.3.
The ordering parameter in this example is used to describe the ordering
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def maxListImpParm[T](elements: List[T])
(implicit ordering: Ordering[T]): T =
elements match {
case List() =>
throw new IllegalArgumentException("empty list!")
case List(x) => x
case x :: rest =>
val maxRest = maxListImpParm(rest)(ordering)
if (ordering.gt(x, maxRest)) x
else maxRest
}
Listing 21.3 · A function with an implicit parameter.
of Ts. In the body of maxListImpParm, this ordering is used in two places: a
recursive call to maxListImpParm, and an if expression that checks whether
the head of the list is larger than the maximum element of the rest of the list.
The maxListImpParm function is an example of an implicit parameter
used to provide more information about a type mentioned explicitly in an earlier parameter list. To be specific, the implicit parameter ordering, of type
Ordering[T], provides more information about type T—in this case, how to
order Ts. Type T is mentioned in List[T], the type of parameter elements,
which appears in the earlier parameter list. Because elements must always
be provided explicitly in any invocation of maxListImpParm, the compiler
will know T at compile time and can therefore determine whether an implicit
definition of type Ordering[T] is available. If so, it can pass in the second
parameter list, ordering, implicitly.
This pattern is so common that the standard Scala library provides implicit “ordering” methods for many common types. You could therefore use
this maxListImpParm method with a variety of types:
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scala> maxListImpParm(List(1,5,10,3))
res9: Int = 10
scala> maxListImpParm(List(1.5, 5.2, 10.7, 3.14159))
res10: Double = 10.7
scala> maxListImpParm(List("one", "two", "three"))
res11: String = two
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In the first case, the compiler inserted an ordering for Ints; in the second
case, for Doubles; in the third case, for Strings.
A style rule for implicit parameters
As a style rule, it is best to use a custom named type in the types of implicit
parameters. For example, the types of prompt and drink in the previous
example was not String, but PreferredPrompt and PreferredDrink, respectively. As a counterexample, consider that the maxListImpParm function could just as well have been written with the following type signature:
def maxListPoorStyle[T](elements: List[T])
(implicit orderer: (T, T) => Boolean): T
To use this version of the function, though, the caller would have to supply an
orderer parameter of type (T, T) => Boolean. This is a fairly generic type
that includes any function from two Ts to a Boolean. It does not indicate
anything at all about what the type is for; it could be an equality test, a lessthan test, a greater-than test, or something else entirely.
The actual code for maxListImpParm, given in Listing 21.3, shows better style. It uses an ordering parameter of type Ordering[T]. The word
Ordering in this type indicates exactly what the implicit parameter is used
for: it is for ordering elements of T. Because this ordering type is more
explicit, it’s no trouble to add implicit providers for this type in the standard
library. To contrast, imagine the chaos that would ensue if you added an
implicit of type (T, T) => Boolean in the standard library, and the compiler
started sprinkling it around in people’s code. You would end up with code
that compiles and runs, but that does fairly arbitrary tests against pairs of
items! Thus the style rule: Use at least one role-determining name within
the type of an implicit parameter.
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21.6
Context bounds
The previous example showed an opportunity to use an implicit but did not.
Note that when you use implicit on a parameter, not only will the compiler
try to supply that parameter with an implicit value, but the compiler will also
use that parameter as an available implicit in the body of the method! Thus,
the first use of ordering within the body of the method can be left out.
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def maxList[T](elements: List[T])
(implicit ordering: Ordering[T]): T =
elements match {
case List() =>
throw new IllegalArgumentException("empty list!")
case List(x) => x
case x :: rest =>
val maxRest = maxList(rest)
// (ordering) is implicit
if (ordering.gt(x, maxRest)) x
// this ordering is
else maxRest
// still explicit
}
Listing 21.4 · A function that uses an implicit parameter internally.
When the compiler examines the code in Listing 21.4, it will see that
the types do not match up. The expression maxList(rest) only supplies
one parameter list, but maxList requires two. Since the second parameter list is implicit, the compiler does not give up type checking immediately. Instead, it looks for an implicit parameter of the appropriate type,
in this case Ordering[T]. In this case, it finds one and rewrites the call to
maxList(rest)(ordering), after which the code type checks.
There is also a way to eliminate the second use of ordering. It involves
the following method defined in the standard library:
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def implicitly[T](implicit t: T) = t
The effect of calling implicitly[Foo] is that the compiler will look for
an implicit definition of type Foo. It will then call the implicitly method
with that object, which in turn returns the object right back. Thus you can
write implicitly[Foo] whenever you want to find an implicit object of
type Foo in the current scope. For example, Listing 21.5 shows a use of
implicitly[Ordering[T]] to retrieve the ordering parameter by its type.
Look closely at this last version of maxList. There is not a single mention of the ordering parameter in the text of the method. The second parameter could just as well be named “comparator”:
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def maxList[T](elements: List[T])
(implicit ordering: Ordering[T]): T =
elements match {
case List() =>
throw new IllegalArgumentException("empty list!")
case List(x) => x
case x :: rest =>
val maxRest = maxList(rest)
if (implicitly[Ordering[T]].gt(x, maxRest)) x
else maxRest
}
Listing 21.5 · A function that uses implicitly.
def maxList[T](elements: List[T])
(implicit comparator: Ordering[T]): T = // same body...
For that matter, this version works as well:
def maxList[T](elements: List[T])
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(implicit iceCream: Ordering[T]): T = // same body...
Because this pattern is common, Scala lets you leave out the name of this
parameter and shorten the method header by using a context bound. Using
a context bound, you would write the signature of maxList as shown in
Listing 21.6. The syntax [T : Ordering] is a context bound, and it does two
things. First, it introduces a type parameter T as normal. Second, it adds an
implicit parameter of type Ordering[T]. In previous versions of maxList,
that parameter was called ordering, but when using a context bound you
don’t know what the parameter will be called. As shown earlier, you often
don’t need to know what the parameter is called.
Intuitively, you can think of a context bound as saying something about
a type parameter. When you write [T <: Ordered[T]] you are saying that
a T is an Ordered[T]. To contrast, when you write [T : Ordering] you
are not so much saying what T is; rather, you are saying that there is some
form of ordering associated with T. Thus, a context bound is quite flexible.
It allows you to use code that requires orderings—or any other property of a
type—without having to change the definition of that type.
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def maxList[T : Ordering](elements: List[T]): T =
elements match {
case List() =>
throw new IllegalArgumentException("empty list!")
case List(x) => x
case x :: rest =>
val maxRest = maxList(rest)
if (implicitly[Ordering[T]].gt(x, maxRest)) x
else maxRest
}
Listing 21.6 · A function with a context bound.
21.7
When multiple conversions apply
It can happen that multiple implicit conversions are in scope and each would
work. For the most part, Scala refuses to insert a conversion in such a case.
Implicits work well when the conversion left out is completely obvious and
pure boilerplate. If multiple conversions apply, then the choice isn’t so obvious after all.
Here’s a simple example. There is a method that takes a sequence, a
conversion that turns an integer into a range, and a conversion that turns an
integer into a list of digits:
scala> def printLength(seq: Seq[Int]) = println(seq.length)
printLength: (seq: Seq[Int])Unit
scala> implicit def intToRange(i: Int) = 1 to i
intToRange: (i:
Int)scala.collection.immutable.Range.Inclusive
scala> implicit def intToDigits(i: Int) =
i.toString.toList.map(_.toInt)
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intToDigits: (i: Int)List[Int]
scala> printLength(12)
:26: error: type mismatch;
found
: Int(12)
required: Seq[Int]
Note that implicit conversions are not applicable because
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they are ambiguous:
both method intToRange of type (i:
Int)scala.collection.immutable.Range.Inclusive
and method intToDigits of type (i: Int)List[Int]
are possible conversion functions from Int(12) to Seq[Int]
printLength(12)
ˆ
The ambiguity here is real. Converting an integer to a sequence of digits
is completely different from converting it to a range. In this case, the programmer should specify which one is intended and be explicit. Up through
Scala 2.7, that was the end of the story. Whenever multiple implicit conversions applied, the compiler refused to choose between them. The situation
was just as with method overloading. If you try to call foo(null) and there
are two different foo overloads that accept null, the compiler will refuse. It
will say that the method call’s target is ambiguous.
Scala 2.8 loosened this rule. If one of the available conversions is strictly
more specific than the others, then the compiler will choose the more specific
one. The idea is that whenever there is a reason to believe a programmer
would always choose one of the conversions over the others, don’t require
the programmer to write it explicitly. After all, method overloading has the
same relaxation. Continuing the previous example, if one of the available
foo methods takes a String while the other takes an Any, then choose the
String version. It’s clearly more specific.
To be more precise, one implicit conversion is more specific than another
if one of the following applies:
• The argument type of the former is a subtype of the latter’s.
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• Both conversions are methods, and the enclosing class of the former
extends the enclosing class of the latter.
The motivation to revisit this issue and revise the rule was to improve
interoperation between Java collections, Scala collections, and strings.
Here’s a simple example:
val cba = "abc".reverse
What is the type inferred for cba? Intuitively, the type should be String.
Reversing a string should yield another string, right? However, in Scala 2.7,
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what happened was that "abc" was converted to a Scala collection. Reversing
a Scala collection yields a Scala collection, so the type of cba would be
a collection. There’s also an implicit conversion back to a string, but that
didn’t patch up every problem. For example, in versions prior to Scala 2.8,
"abc" == "abc".reverse.reverse was false!
With Scala 2.8, the type of cba is String. The old implicit conversion to
a Scala collection (now named WrappedString) is retained. However, there
is a more specific conversion supplied from String to a new type called
StringOps. StringOps has many methods such as reverse, but instead of
returning a collection, they return a String. The conversion to StringOps
is defined directly in Predef, whereas the conversion to a Scala collection
is defined in a new class, LowPriorityImplicits, which is extended by
Predef. Whenever a choice exists between these two conversions, the compiler chooses the conversion to StringOps, because it’s defined in a subclass
of the class where the other conversion is defined.
21.8
Debugging implicits
Implicits are a powerful feature in Scala, but one that’s sometimes difficult
to get right. This section contains a few tips for debugging implicits.
Sometimes you might wonder why the compiler did not find an implicit
conversion that you think should apply. In that case it helps to write the
conversion out explicitly. If that also gives an error message, you then know
why the compiler could not apply your implicit.
For instance, assume that you mistakenly took wrapString to be a conversion from Strings to Lists, instead of IndexedSeqs. You would wonder
why the following code does not work:
scala> val chars: List[Char] = "xyz"
:24: error: type mismatch;
found
: String("xyz")
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required: List[Char]
val chars: List[Char] = "xyz"
ˆ
Again, it helps to write the wrapString conversion explicitly to find out
what went wrong:
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scala> val chars: List[Char] = wrapString("xyz")
:24: error: type mismatch;
found
: scala.collection.immutable.WrappedString
required: List[Char]
val chars: List[Char] = wrapString("xyz")
ˆ
With this, you have found the cause of the error: wrapString has the wrong
return type. On the other hand, it’s also possible that inserting the conversion
explicitly will make the error go away. In that case you know that one of the
other rules (such as the Scope Rule) was preventing the implicit conversion
from being applied.
When you are debugging a program, it can sometimes help to see what
implicit conversions the compiler is inserting. The -Xprint:typer option to
the compiler is useful for this. If you run scalac with this option, the compiler will show you what your code looks like after all implicit conversions
have been added by the type checker. An example is shown in Listing 21.7
and Listing 21.8. If you look at the last statement in each of these listings,
you’ll see that the second parameter list to enjoy, which was left off in the
code in Listing 21.7, “enjoy("reader"),” was inserted by the compiler, as
shown in Listing 21.8:
Mocha.this.enjoy("reader")(Mocha.this.pref)
If you are brave, try scala -Xprint:typer to get an interactive shell
that prints out the post-typing source code it uses internally. If you do so, be
prepared to see an enormous amount of boilerplate surrounding the meat of
your code.
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21.9
Conclusion
Implicits are a powerful, code-condensing feature of Scala. This chapter has
shown you Scala’s rules about implicits and several common programming
situations where you can profit from using implicits.
As a word of warning, implicits can make code confusing if they are
used too frequently. Thus, before adding a new implicit conversion, first
ask whether you can achieve a similar effect through other means, such as
inheritance, mixin composition, or method overloading. If all of these fail,
however, and you feel like a lot of your code is still tedious and redundant,
then implicits might just be able to help you out.
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object Mocha extends App {
class PreferredDrink(val preference: String)
implicit val pref = new PreferredDrink("mocha")
def enjoy(name: String)(implicit drink: PreferredDrink) = {
print("Welcome, " + name)
print(". Enjoy a ")
print(drink.preference)
println("!")
}
enjoy("reader")
}
Listing 21.7 · Sample code that uses an implicit parameter.
$ scalac -Xprint:typer mocha.scala
[[syntax trees at end of typer]]// Scala source: mocha.scala
package {
final object Mocha extends java.lang.Object with Application
with ScalaObject {
// ...
private[this] val pref: Mocha.PreferredDrink =
new Mocha.this.PreferredDrink("mocha");
implicit
def pref: Mocha.PreferredDrink = Mocha.this.pref;
def enjoy(name: String)
(implicit drink: Mocha.PreferredDrink): Unit = {
scala.this.Predef.print("Welcome, ".+(name));
scala.this.Predef.print(". Enjoy a ");
scala.this.Predef.print(drink.preference);
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scala.this.Predef.println("!")
};
Mocha.this.enjoy("reader")(Mocha.this.pref)
}
}
Listing 21.8 · Sample code after type checking and insertion of implicits.
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Chapter 22
Implementing Lists
Lists have been ubiquitous in this book. Class List is probably the most
commonly used structured data type in Scala. Chapter 16 showed you how
to use lists. This chapter “opens up the covers” and explains a bit about how
lists are implemented in Scala.
Knowing the internal workings of the List class is useful for several
reasons. You gain a better idea of the relative efficiency of list operations,
which will help you in writing fast and compact code using lists. You also
gain a toolbox of techniques that you can apply in the design of your own
libraries. Finally, the List class is a sophisticated application of Scala’s type
system in general and its genericity concepts in particular. So studying class
List will deepen your knowledge in these areas.
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22.1
The List class in principle
Lists are not “built-in” as a language construct in Scala; they are defined by
an abstract class List in the scala package, which comes with two subclasses for :: and Nil. In this chapter we will present a quick tour through
class List. This section presents a somewhat simplified account of the class,
compared to its real implementation in the Scala standard library, which is
covered in Section 22.3.
package scala
abstract class List[+T] {
List is an abstract class, so you cannot define elements by calling the empty
List constructor. For instance the expression “new List” would be ille-
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Section 22.1
Chapter 22 · Implementing Lists
scala
List[+T]
«sealed abstract»
scala
scala
::[T]
Nil
«final case»
«case object»
Figure 22.1 · Class hierarchy for Scala lists.
gal. The class has a type parameter T. The + in front of this type parameter
specifies that lists are covariant, as discussed in Chapter 19.
Because of this property, you can assign a value of type List[Int] to a
variable of type List[Any]:
scala> val xs = List(1, 2, 3)
xs: List[Int] = List(1, 2, 3)
scala> var ys: List[Any] = xs
ys: List[Any] = List(1, 2, 3)
All list operations can be defined in terms of three basic methods:
def isEmpty: Boolean
def head: T
def tail: List[T]
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These three methods are all abstract in class List. They are defined in the
subobject Nil and the subclass ::. The hierarchy for List is shown in Figure 22.1.
The Nil object
The Nil object defines an empty list. Its definition is shown in Listing 22.1.
The Nil object inherits from type List[Nothing]. Because of covariance,
this means that Nil is compatible with every instance of the List type.
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case object Nil extends List[Nothing] {
override def isEmpty = true
def head: Nothing =
throw new NoSuchElementException("head of empty list")
def tail: List[Nothing] =
throw new NoSuchElementException("tail of empty list")
}
Listing 22.1 · The definition of the Nil singleton object.
The three abstract methods of class List are implemented in the Nil
object in a straightforward way: The isEmpty method returns true, and the
head and tail methods both throw an exception. Note that throwing an
exception is not only reasonable, but practically the only possible thing to do
for head: Because Nil is a List of Nothing, the result type of head must
be Nothing. Since there is no value of this type, this means that head cannot
return a normal value. It has to return abnormally by throwing an exception.1
The :: class
Class ::, pronounced “cons” for “construct,” represents non-empty lists. It’s
named that way in order to support pattern matching with the infix ::. You
have seen in Section 16.5 that every infix operation in a pattern is treated as a
constructor application of the infix operator to its arguments. So the pattern
x :: xs is treated as ::(x, xs) where :: is a case class.
Here is the definition of the :: class:
final case class ::[T](hd: T, tl: List[T]) extends List[T] {
def head = hd
def tail = tl
override def isEmpty: Boolean = false
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}
The implementation of the :: class is straightforward. It takes two parameters hd and tl, representing the head and the tail of the list to be constructed.
1 To be precise, the types would also permit for head to always go into an infinite loop
instead of throwing an exception, but this is clearly not what’s wanted.
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The definitions of the head and tail method simply return the corresponding parameter. In fact, this pattern can be abbreviated by letting the parameters directly implement the head and tail methods of the superclass List,
as in the following equivalent but shorter definition of the :: class:
final case class ::[T](head: T, tail: List[T])
extends List[T] {
override def isEmpty: Boolean = false
}
This works because every case class parameter is implicitly also a field of the
class (it’s like the parameter declaration was prefixed with val). Recall from
Section 20.3 that Scala allows you to implement an abstract parameterless
method such as head or tail with a field. So the code above directly uses
the parameters head and tail as implementations of the abstract methods
head and tail that were inherited from class List.
Some more methods
All other List methods can be written using the basic three. For instance:
def length: Int =
if (isEmpty) 0 else 1 + tail.length
or:
def drop(n: Int): List[T] =
if (isEmpty) Nil
else if (n <= 0) this
else tail.drop(n - 1)
or:
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def map[U](f: T => U): List[U] =
if (isEmpty) Nil
else f(head) :: tail.map(f)
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List construction
The list construction methods :: and ::: are special. Because they end in
a colon, they are bound to their right operand. That is, an operation such
as x :: xs is treated as the method call xs.::(x), not x.::(xs). In fact,
x.::(xs) would not make sense, as x is of the list element type, which can
be arbitrary, so we cannot assume that this type would have a :: method.
For this reason, the :: method should take an element value and yield
a new list. What is the required type of the element value? You might be
tempted to say it should be the same as the list’s element type, but in fact this
is more restrictive than necessary.
To see why, consider this class hierarchy:
abstract class Fruit
class Apple extends Fruit
class Orange extends Fruit
Listing 22.2 shows what happens when you construct lists of fruit:
scala> val apples = new Apple :: Nil
apples: List[Apple] = List(Apple@e885c6a)
scala> val fruits = new Orange :: apples
fruits: List[Fruit] = List(Orange@3f51b349, Apple@e885c6a)
Listing 22.2 · Prepending a supertype element to a subtype list.
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The apples value is treated as a List of Apples, as expected. However,
the definition of fruits shows that it’s still possible to add an element of
a different type to that list. The element type of the resulting list is Fruit,
which is the most precise common supertype of the original list element
type (i.e., Apple) and the type of the element to be added (i.e., Orange).
This flexibility is obtained by defining the :: method (cons) as shown in
Listing 22.3:
def ::[U >: T](x: U): List[U] = new scala.::(x, this)
Listing 22.3 · The definition of method :: (cons) in class List.
Note that the method is itself polymorphic—it takes a type parameter
named U. Furthermore, U is constrained in [U >: T] to be a supertype of the
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504
Orange
head
head
::
::
tail
fruits
Apple
tail
apples
Nil
Figure 22.2 · The structure of the Scala lists shown in Listing 22.2.
list element type T. The element to be added is required to be of type U and
the result is a List[U].
With the formulation of :: shown in Listing 22.3, you can check how the
definition of fruits shown in Listing 22.2 works out type-wise: In that definition, the type parameter U of :: is instantiated to Fruit. The lower-bound
constraint of U is satisfied because the list apples has type List[Apple]
and Fruit is a supertype of Apple. The argument to the :: is new Orange,
which conforms to type Fruit. Therefore, the method application is typecorrect with result type List[Fruit]. Figure 22.2 illustrates the structure
of the lists that result from executing the code shown in Listing 22.2.
In fact, the polymorphic definition of :: with the lower bound T is not
only convenient, it is also necessary to render the definition of class List
type-correct. This is because Lists are defined to be covariant.
Assume for a moment that we had defined :: like this:
// A thought experiment (which wouldn't work)
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def ::(x: T): List[T] = new scala.::(x, this)
You saw in Chapter 19 that method parameters count as contravariant positions, so the list element type T is in contravariant position in the definition
above. But then List cannot be declared covariant in T. The lower bound
[U >: T] thus kills two birds with one stone: It removes a typing problem
and leads to a :: method that’s more flexible to use. The list concatenation
method ::: is defined in a similar way to ::, as shown in Listing 22.4.
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Section 22.2
Chapter 22 · Implementing Lists
def :::[U >: T](prefix: List[U]): List[U] =
if (prefix.isEmpty) this
else prefix.head :: prefix.tail ::: this
Listing 22.4 · The definition of method ::: in class List.
Like cons, concatenation is polymorphic. The result type is “widened”
as necessary to include the types of all list elements. Note again that the
order of the arguments is swapped between an infix operation and an explicit
method call. Because both ::: and :: end in a colon, they both bind to
the right and are both right associative. For instance, the else part of the
definition of ::: shown in Listing 22.4 contains infix operations of both ::
and :::.
These infix operations can be expanded to equivalent method calls as
follows:
prefix.head :: prefix.tail ::: this
equals (because :: and ::: are right-associative)
prefix.head :: (prefix.tail ::: this)
equals (because :: binds to the right)
(prefix.tail ::: this).::(prefix.head)
equals (because ::: binds to the right)
this.:::(prefix.tail).::(prefix.head)
22.2
The ListBuffer class
The typical access pattern for a list is recursive. For instance, to increment
every element of a list without using map you could write:
def incAll(xs: List[Int]): List[Int] = xs match {
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case List() => List()
case x :: xs1 => x + 1 :: incAll(xs1)
}
One shortcoming of this program pattern is that it is not tail recursive. Note
that the recursive call to incAll above occurs inside a :: operation. Therefore each recursive call requires a new stack frame.
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Section 22.2
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On today’s virtual machines this means that you cannot apply incAll
to lists of much more than about 30,000 to 50,000 elements. This is a pity.
How do you write a version of incAll that can work on lists of arbitrary size
(as much as heap-capacity allows)?
One approach is to use a loop:
for (x <- xs) // ??
But what should go in the loop body? Note that where incAll constructs
the list by prepending elements to the result of the recursive call, the loop
needs to append new elements at the end of the result list. A very inefficient
possibility is to use :::, the list append operator:
var result = List[Int]()
// a very inefficient approach
for (x <- xs) result = result ::: List(x + 1)
result
This is terribly inefficient. Because ::: takes time proportional to the length
of its first operand, the whole operation takes time proportional to the square
of the length of the list. This is clearly unacceptable.
A better alternative is to use a list buffer. List buffers let you accumulate
the elements of a list. To do this, you use an operation such as “buf += elem”,
which appends the element elem at the end of the list buffer buf. Once you
are done appending elements, you can turn the buffer into a list using the
toList operation.
ListBuffer is a class in package scala.collection.mutable. To use
the simple name only, you can import ListBuffer from its package:
import scala.collection.mutable.ListBuffer
Using a list buffer, the body of incAll can now be written as follows:
val buf = new ListBuffer[Int]
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for (x <- xs) buf += x + 1
buf.toList
This is a very efficient way to build lists. In fact, the list buffer implementation is organized so that both the append operation (+=) and the toList
operation take (very short) constant time.
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Section 22.3
22.3
Chapter 22 · Implementing Lists
The List class in practice
The implementations of list methods given in Section 22.1 are concise and
clear, but suffer from the same stack overflow problem as the non-tail recursive implementation of incAll. Therefore, most methods in the real implementation of class List avoid recursion and use loops with list buffers
instead. For example, Listing 22.5 shows the real implementation of map in
class List:
final override def map[U](f: T => U): List[U] = {
val b = new ListBuffer[U]
var these = this
while (!these.isEmpty) {
b += f(these.head)
these = these.tail
}
b.toList
}
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Listing 22.5 · The definition of method map in class List.
This revised implementation traverses the list with a simple loop, which
is highly efficient. A tail recursive implementation would be similarly efficient, but a general recursive implementation would be slower and less
scalable. But what about the operation b.toList at the end? What is its
complexity? In fact, the call to the toList method takes only a small number of cycles, which is independent of the length of the list.
To understand why, take a second look at class ::, which constructs
non-empty lists. In practice, this class does not quite correspond to its idealized definition given previously in Section 22.1. The real definition is
shown in Listing 22.6. As you can see, there’s one peculiarity: the tl argument is a var! This means that it is possible to modify the tail of a list
after the list is constructed. However, because the variable tl has the modifier private[scala], it can be accessed only from within package scala.
Client code outside this package can neither read nor write tl.
Since the ListBuffer class is contained in a subpackage of package
scala, scala.collection.mutable, ListBuffer can access the tl field
of a cons cell. In fact the elements of a list buffer are represented as a list and
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Section 22.3
Chapter 22 · Implementing Lists
final case class ::[U](hd: U,
private[scala] var tl: List[U]) extends List[U] {
def head = hd
def tail = tl
override def isEmpty: Boolean = false
}
Listing 22.6 · The definition of the :: subclass of List.
appending new elements involves a modification of the tl field of the last ::
cell in that list. Here’s the start of class ListBuffer:
package scala.collection.immutable
final class ListBuffer[T] extends Buffer[T] {
private var start: List[T] = Nil
private var last0: ::[T] = _
private var exported: Boolean = false
...
You see three private fields that characterize a ListBuffer:
start
last0
exported
points to the list of all elements stored in the buffer
points to the last :: cell in that list
indicates whether the buffer has been turned into
a list using a toList operation
The toList operation is very simple:
override def toList: List[T] = {
exported = !start.isEmpty
start
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}
It returns the list of elements referred to by start and also sets exported
to true if that list is nonempty. So toList is very efficient because it does
not copy the list which is stored in a ListBuffer. But what happens if the
list is further extended after the toList operation? Of course, once a list
is returned from toList, it must be immutable. However, appending to the
last0 element will modify the list which is referred to by start.
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Section 22.4
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To maintain the correctness of the list buffer operations, you need to work
on a fresh list instead. This is achieved by the first line in the implementation
of the += operation:
override def += (x: T) = {
if (exported) copy()
if (start.isEmpty) {
last0 = new scala.::(x, Nil)
start = last0
} else {
val last1 = last0
last0 = new scala.::(x, Nil)
last1.tl = last0
}
}
You see that += copies the list pointed to by start if exported is true. So,
in the end, there is no free lunch. If you want to go from lists which can
be extended at the end to immutable lists, there needs to be some copying.
However, the implementation of ListBuffer is such that copying is necessary only for list buffers that are further extended after they have been turned
into lists. This case is quite rare in practice. Most use cases of list buffers
add elements incrementally and then do one toList operation at the end. In
such cases, no copying is necessary.
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22.4
Functional on the outside
In the previous section, we showed key elements of the implementation of
Scala’s List and ListBuffer classes. You saw that lists are purely functional on the “outside” but have an imperative implementation using list
buffers on the “inside.” This is a typical strategy in Scala programming—
trying to combine purity with efficiency by carefully delimiting the effects
of impure operations.
But you might ask, Why insist on purity? Why not just open up the
definition of lists, making the tail field, and maybe also the head field, mutable? The disadvantage of such an approach is that it would make programs
much more fragile. Note that constructing lists with :: re-uses the tail of the
constructed list.
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So when you write:
val ys = 1 :: xs
val zs = 2 :: xs
the tails of lists ys and zs are shared; they point to the same data structure.
This is essential for efficiency; if the list xs was copied every time you added
a new element onto it, this would be much slower. Because sharing is pervasive, changing list elements, if it were possible, would be quite dangerous.
For instance, taking the code above, if you wanted to truncate list ys to its
first two elements by writing:
ys.drop(2).tail = Nil
// can't do this in Scala!
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you would also truncate lists zs and xs as a side effect.
Clearly, it would be quite difficult to keep track of what gets changed.
That’s why Scala opts for pervasive sharing and no mutation for lists. The
ListBuffer class still allows you to build up lists imperatively and incrementally, if you wish. But since list buffers are not lists, the types keep
mutable buffers and immutable lists separate.
The design of Scala’s List and ListBuffer is quite similar to what’s
done in Java’s pair of classes String and StringBuffer. This is no coincidence. In both situations the designers wanted to maintain a pure immutable
data structure but also provide an efficient way to construct this structure
incrementally. For Java and Scala strings, StringBuffers (or, in Java 5,
StringBuilders) provide a way to construct a string incrementally. For
Scala’s lists, you have a choice: You can either construct lists incrementally by adding elements to the beginning of a list using ::, or you use a
list buffer for adding elements to the end. Which one is preferable depends
on the situation. Usually, :: lends itself well to recursive algorithms in the
divide-and-conquer style. List buffers are often used in a more traditional
loop-based style.
22.5
Conclusion
In this chapter, you saw how lists are implemented in Scala. List is one of
the most heavily used data structures in Scala, and it has a refined implementation. List’s two subclasses, Nil and ::, are both case classes. Instead of
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Section 22.5
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recursing through this structure, however, many core list methods are implemented using a ListBuffer. ListBuffer, in turn, is carefully implemented
so that it can efficiently build lists without allocating extraneous memory.
It is functional on the outside, but uses mutation internally to speed up the
common case where a buffer is discarded after toList has been called. After studying all of this, you now know the list classes inside and out, and you
might have learned an implementation trick or two.
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511
Chapter 23
For Expressions Revisited
Chapter 16 demonstrated that higher-order functions, such as map, flatMap,
and filter, provide powerful constructions for dealing with lists. But sometimes the level of abstraction required by these functions makes a program a
bit hard to understand.
Here’s an example. Say you are given a list of persons, each defined as an
instance of a class Person. Class Person has fields indicating the person’s
name, whether he or she is male, and his or her children.
Here’s the class definition:
scala> case class Person(name: String,
isMale: Boolean,
children: Person*)
Here’s a list of some sample persons:
val lara = Person("Lara", false)
val bob = Person("Bob", true)
val julie = Person("Julie", false, lara, bob)
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val persons = List(lara, bob, julie)
Now, say you want to find out the names of all pairs of mothers and their
children in that list. Using map, flatMap and filter, you can formulate the
following query:
scala> persons filter (p => !p.isMale) flatMap (p =>
(p.children map (c => (p.name, c.name))))
res0: List[(String, String)] = List((Julie,Lara),
(Julie,Bob))
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Section 23.1
Chapter 23 · For Expressions Revisited
You could optimize this example a bit by using a withFilter call instead of
filter. This would avoid the creation of an intermediate data structure for
female persons:
scala> persons withFilter (p => !p.isMale) flatMap (p =>
(p.children map (c => (p.name, c.name))))
res1: List[(String, String)] = List((Julie,Lara),
(Julie,Bob))
These queries do their job, but they are not exactly trivial to write or understand. Is there a simpler way? In fact, there is. Remember the for
expressions in Section 7.3? Using a for expression, the same example can
be written as follows:
scala> for (p <- persons; if !p.isMale; c <- p.children)
yield (p.name, c.name)
res2: List[(String, String)] = List((Julie,Lara),
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(Julie,Bob))
The result of this expression is exactly the same as the result of the previous
expression. What’s more, most readers of the code would likely find the
for expression much clearer than the previous query, which used the higherorder functions, map, flatMap, and withFilter.
However, the last two queries are not as dissimilar as it might seem.
In fact, it turns out that the Scala compiler will translate the second query
into the first one. More generally, all for expressions that yield a result are translated by the compiler into combinations of invocations of the
higher-order methods map, flatMap, and withFilter. All for loops without yield are translated into a smaller set of higher-order functions: just
withFilter and foreach.
In this chapter, you’ll find out first about the precise rules of writing for
expressions. After that, you’ll see how they can make combinatorial problems easier to solve. Finally, you’ll learn how for expressions are translated,
and how as a result, for expressions can help you “grow” the Scala language
into new application domains.
23.1
For expressions
Generally, a for expression is of the form:
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for ( seq ) yield expr
Here, seq is a sequence of generators, definitions, and filters, with semicolons between successive elements. An example is the for expression:
for (p <- persons; n = p.name; if (n startsWith "To"))
yield n
This for expression contains one generator, one definition, and one filter. As
mentioned in Section 7.3 on page 163, you can also enclose the sequence in
braces instead of parentheses. Then the semicolons become optional:
for {
p <- persons
// a generator
n = p.name
// a definition
if (n startsWith "To")
// a filter
} yield n
A generator is of the form:
pat <- expr
The expression expr typically returns a list, even though you will see later
that this can be generalized. The pattern pat gets matched one-by-one against
all elements of that list. If the match succeeds, the variables in the pattern get
bound to the corresponding parts of the element, just the way it is described
in Chapter 15. But if the match fails, no MatchError is thrown. Instead, the
element is simply discarded from the iteration.
In the most common case, the pattern pat is just a variable x, as in
x <- expr. In that case, the variable x simply iterates over all elements
returned by expr.
A definition is of the form:
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pat = expr
This definition binds the pattern pat to the value of expr, so it has the same
effect as a val definition:
val x = expr
The most common case is again where the pattern is a simple variable x (e.g.,
x = expr). This defines x as a name for the value expr.
A filter is of the form:
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if expr
Here, expr is an expression of type Boolean. The filter drops from the iteration all elements for which expr returns false.
Every for expression starts with a generator. If there are several generators in a for expression, later generators vary more rapidly than earlier ones.
You can verify this easily with the following simple test:
scala> for (x <- List(1, 2); y <- List("one", "two"))
yield (x, y)
res3: List[(Int, String)] =
List((1,one), (1,two), (2,one), (2,two))
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23.2
The n-queens problem
A particularly suitable application area of for expressions are combinatorial
puzzles. An example of such a puzzle is the 8-queens problem: Given a
standard chess-board, place eight queens such that no queen is in check from
any other (a queen can check another piece if they are on the same column,
row, or diagonal). To find a solution to this problem, it’s actually simpler to
generalize it to chess-boards of arbitrary size. Hence, the problem is to place
N queens on a chess-board of N × N squares, where the size N is arbitrary.
We’ll start numbering cells at one, so the upper-left cell of an N × N board
has coordinate (1, 1) and the lower-right cell has coordinate (N, N).
To solve the N-queens problem, note that you need to place a queen in
each row. So you could place queens in successive rows, each time checking
that a newly placed queen is not in check from any other queens that have
already been placed. In the course of this search, it might happen that a
queen that needs to be placed in row k would be in check in all fields of that
row from queens in row 1 to k − 1. In that case, you need to abort that part
of the search in order to continue with a different configuration of queens in
columns 1 to k − 1.
An imperative solution to this problem would place queens one by one,
moving them around on the board. But it looks difficult to come up with a
scheme that really tries all possibilities. A more functional approach represents a solution directly, as a value. A solution consists of a list of coordinates, one for each queen placed on the board. Note, however, that a full
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solution can not be found in a single step. It needs to be built up gradually,
by occupying successive rows with queens.
This suggests a recursive algorithm. Assume you have already generated
all solutions of placing k queens on a board of size N ×N, where k is less than
N. Each such solution can be presented by a list of length k of coordinates
(row, column), where both row and column numbers range from 1 to N. It’s
convenient to treat these partial solution lists as stacks, where the coordinates
of the queen in row k come first in the list, followed by the coordinates of
the queen in row k − 1, and so on. The bottom of the stack is the coordinate
of the queen placed in the first row of the board. All solutions together are
represented as a list of lists, with one element for each solution.
Now, to place the next queen in row k + 1, generate all possible extensions of each previous solution by one more queen. This yields another list
of solution lists, this time of length k + 1. Continue the process until you
have obtained all solutions of the size of the chess-board N.
This algorithmic idea is embodied in function placeQueens below:
def queens(n: Int): List[List[(Int, Int)]] = {
def placeQueens(k: Int): List[List[(Int, Int)]] =
if (k == 0)
List(List())
else
for {
queens <- placeQueens(k - 1)
column <- 1 to n
queen = (k, column)
if isSafe(queen, queens)
} yield queen :: queens
placeQueens(n)
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}
The outer function queens in the program above simply calls placeQueens
with the size of the board n as its argument. The task of the function application placeQueens(k) is to generate all partial solutions of length k in a list.
Every element of the list is one solution, represented by a list of length k. So
placeQueens returns a list of lists.
If the parameter k to placeQueens is 0, this means that it needs to generate all solutions of placing zero queens on zero rows. There is only one such
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solution: place no queen at all. This solution is represented by the empty
list. So if k is zero, placeQueens returns List(List()), a list consisting
of a single element that is the empty list. Note that this is quite different
from the empty list List(). If placeQueens returns List(), this means no
solutions, instead of a single solution consisting of no placed queens.
In the other case, where k is not zero, all the work of placeQueens is
done in a for expression. The first generator of that for expression iterates
through all solutions of placing k - 1 queens on the board. The second generator iterates through all possible columns on which the k’th queen might
be placed. The third part of the for expression defines the newly considered queen position to be the pair consisting of row k and each produced
column. The fourth part of the for expression is a filter which checks with
isSafe whether the new queen is safe from check by all previous queens
(the definition of isSafe will be discussed a bit later).
If the new queen is not in check from any other queens, it can form part of
a partial solution, so placeQueens generates with queen :: queens a new
solution. If the new queen is not safe from check, the filter returns false, so
no solution is generated.
The only remaining bit is the isSafe method, which is used to check
whether a given queen is in check from any other element in a list of queens.
Here is its definition:
def isSafe(queen: (Int, Int), queens: List[(Int, Int)]) =
queens forall (q => !inCheck(queen, q))
def inCheck(q1: (Int, Int), q2: (Int, Int)) =
q1._1 == q2._1 ||
// same row
q1._2 == q2._2 ||
// same column
(q1._1 - q2._1).abs == (q1._2 - q2._2).abs // on diagonal
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The isSafe method expresses that a queen is safe with respect to some other
queens if it is not in check from any other queen. The inCheck method
expresses that queens q1 and q2 are mutually in check.
It returns true in one of three cases:
1. If the two queens have the same row coordinate,
2. If the two queens have the same column coordinate,
3. If the two queens are on the same diagonal (i.e., the difference between
their rows and the difference between their columns are the same).
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The first case—that the two queens have the same row coordinate—cannot
happen in the application because placeQueens already takes care to place
each queen in a different row. So you could remove the test without changing
the functionality of the program.
23.3
Querying with for expressions
The for notation is essentially equivalent to common operations of database
query languages. For instance, say you are given a database named books,
represented as a list of books, where Book is defined as follows:
case class Book(title: String, authors: String*)
Here is a small example database represented as an in-memory list:
val books: List[Book] =
List(
Book(
"Structure and Interpretation of Computer Programs",
"Abelson, Harold", "Sussman, Gerald J."
),
Book(
"Principles of Compiler Design",
"Aho, Alfred", "Ullman, Jeffrey"
),
Book(
"Programming in Modula-2",
"Wirth, Niklaus"
),
Book(
"Elements of ML Programming",
"Ullman, Jeffrey"
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),
Book(
"The Java Language Specification", "Gosling, James",
"Joy, Bill", "Steele, Guy", "Bracha, Gilad"
)
)
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To find the titles of all books whose author’s last name is “Gosling”:
scala> for (b <- books; a <- b.authors
if a startsWith "Gosling")
yield b.title
res4: List[String] = List(The Java Language Specification)
Or to find the titles of all books that have the string “Program” in their title:
scala> for (b <- books if (b.title indexOf "Program") >= 0)
yield b.title
res5: List[String] = List(Structure and Interpretation of
Computer Programs, Programming in Modula-2, Elements of ML
Programming)
Or to find the names of all authors who have written at least two books in the
database:
scala> for (b1 <- books; b2 <- books if b1 != b2;
a1 <- b1.authors; a2 <- b2.authors if a1 == a2)
yield a1
res6: List[String] = List(Ullman, Jeffrey, Ullman, Jeffrey)
The last solution is still not perfect because authors will appear several times
in the list of results. You still need to remove duplicate authors from result
lists. This can be achieved with the following function:
scala> def removeDuplicates[A](xs: List[A]): List[A] = {
if (xs.isEmpty) xs
else
xs.head :: removeDuplicates(
xs.tail filter (x => x != xs.head)
)
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}
removeDuplicates: [A](xs: List[A])List[A]
scala> removeDuplicates(res6)
res7: List[String] = List(Ullman, Jeffrey)
It’s worth noting that the last expression in method removeDuplicates can
be equivalently expressed using a for expression:
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xs.head :: removeDuplicates(
for (x <- xs.tail if x != xs.head) yield x
)
23.4
Translation of for expressions
Every for expression can be expressed in terms of the three higher-order
functions map, flatMap, and withFilter. This section describes the translation scheme, which is also used by the Scala compiler.
Translating for expressions with one generator
First, assume you have a simple for expression:
for (x <- expr1 ) yield expr2
where x is a variable. Such an expression is translated to:
expr1 .map(x => expr2 )
Translating for expressions starting with a generator and a filter
Now, consider for expressions that combine a leading generator with some
other elements. A for expression of the form:
for (x <- expr1 if expr2 ) yield expr3
is translated to:
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for (x <- expr1 withFilter (x => expr2 )) yield expr3
This translation gives another for expression that is shorter by one element
than the original, because an if element is transformed into an application of
withFilter on the first generator expression. The translation then continues
with this second expression, so in the end you obtain:
expr1 withFilter (x => expr2 ) map (x => expr3 )
The same translation scheme also applies if there are further elements following the filter. If seq is an arbitrary sequence of generators, definitions,
and filters, then:
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for (x <- expr1 if expr2 ; seq) yield expr3
is translated to:
for (x <- expr1 withFilter expr2 ; seq) yield expr3
Then translation continues with the second expression, which is again shorter
by one element than the original one.
Translating for expressions starting with two generators
The next case handles for expressions that start with two generators, as in:
for (x <- expr1 ; y <- expr2 ; seq) yield expr3
Again, assume that seq is an arbitrary sequence of generators, definitions,
and filters. In fact, seq might also be empty, and in that case there would not
be a semicolon after expr2 . The translation scheme stays the same in each
case. The for expression above is translated to an application of flatMap:
expr1 .flatMap(x => for (y <- expr2 ; seq) yield expr3 )
This time, there is another for expression in the function value passed to
flatMap. That for expression (which is again simpler by one element than
the original) is in turn translated with the same rules.
The three translation schemes given so far are sufficient to translate all
for expressions that contain just generators and filters, and where generators
bind only simple variables. Take, for instance, the query, “find all authors
who have published at least two books,” from Section 23.3:
for (b1 <- books; b2 <- books if b1 != b2;
a1 <- b1.authors; a2 <- b2.authors if a1 == a2)
yield a1
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This query translates to the following map/flatMap/filter combination:
books flatMap (b1 =>
books withFilter (b2 => b1 != b2) flatMap (b2 =>
b1.authors flatMap (a1 =>
b2.authors withFilter (a2 => a1 == a2) map (a2 =>
a1))))
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The translation scheme presented so far does not yet handle generators that
bind whole patterns instead of simple variables. It also does not yet cover
definitions. These two aspects will be explained in the next two sub-sections.
Translating patterns in generators
The translation scheme becomes more complicated if the left hand side of
generator is a pattern, pat, other than a simple variable. The case where the
for expression binds a tuple of variables is still relatively easy to handle. In
that case, almost the same scheme as for single variables applies.
A for expression of the form:
for ((x1 , ..., xn ) <- expr1 ) yield expr2
translates to:
expr1 .map { case (x1 , ..., xn ) => expr2 }
Things become a bit more involved if the left hand side of the generator is
an arbitrary pattern pat instead of a single variable or a tuple.
In this case:
for (pat <- expr1 ) yield expr2
translates to:
expr1 withFilter {
case pat => true
case _ => false
} map {
case pat => expr2
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}
That is, the generated items are first filtered and only those that match pat
are mapped. Therefore, it’s guaranteed that a pattern-matching generator
will never throw a MatchError.
The scheme here only treated the case where the for expression contains a single pattern-matching generator. Analogous rules apply if the for
expression contains other generators, filters or definitions. Because these additional rules don’t add much new insight, they are omitted from discussion
here. If you are interested, you can look them up in the Scala Language
Specification [Ode11].
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Translating definitions
The last missing situation is where a for expression contains embedded definitions. Here’s a typical case:
for (x <- expr1 ; y = expr2 ; seq) yield expr3
Assume again that seq is a (possibly empty) sequence of generators, definitions, and filters. This expression is translated to this one:
for ((x, y) <- for (x <- expr1 ) yield (x, expr2 ); seq)
yield expr3
So you see that expr2 is evaluated each time there is a new x value being
generated. This re-evaluation is necessary because expr2 might refer to x
and so needs to be re-evaluated for changing values of x. For you as a programmer, the conclusion is that it’s probably not a good idea to have definitions embedded in for expressions that do not refer to variables bound by
some preceding generator, because re-evaluating such expressions would be
wasteful. For instance, instead of:
for (x <- 1 to 1000; y = expensiveComputationNotInvolvingX)
yield x * y
it’s usually better to write:
val y = expensiveComputationNotInvolvingX
for (x <- 1 to 1000) yield x * y
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Translating for loops
The previous subsections showed how for expressions that contain a yield
are translated. What about for loops that simply perform a side effect without returning anything? Their translation is similar, but simpler than for
expressions. In principle, wherever the previous translation scheme used a
map or a flatMap in the translation, the translation scheme for for loops
uses just a foreach.
For instance, the expression:
for (x <- expr1 ) body
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translates to:
expr1 foreach (x => body)
A larger example is the expression:
for (x <- expr1 ; if expr2 ; y <- expr3 ) body
This expression translates to:
expr1 withFilter (x => expr2 ) foreach (x =>
expr3 foreach (y => body))
For example, the following expression sums up all elements of a matrix represented as a list of lists:
var sum = 0
for (xs <- xss; x <- xs) sum += x
This loop is translated into two nested foreach applications:
var sum = 0
xss foreach (xs =>
xs foreach (x =>
sum += x))
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23.5
Going the other way
The previous section showed that for expressions can be translated into applications of the higher-order functions map, flatMap, and withFilter.
In fact, you could equally go the other way: Every application of a map,
flatMap, or filter can be represented as a for expression.
Here are implementations of the three methods in terms of for expressions. The methods are contained in an object Demo to distinguish them from
the standard operations on Lists. To be concrete, the three functions all take
a List as parameter, but the translation scheme would work just as well with
other collection types:
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object Demo {
def map[A, B](xs: List[A], f: A => B): List[B] =
for (x <- xs) yield f(x)
def flatMap[A, B](xs: List[A], f: A => List[B]): List[B] =
for (x <- xs; y <- f(x)) yield y
def filter[A](xs: List[A], p: A => Boolean): List[A] =
for (x <- xs if p(x)) yield x
}
Not surprisingly, the translation of the for expression used in the body of
Demo.map will produce a call to map in class List. Similarly, Demo.flatMap
and Demo.filter translate to flatMap and withFilter in class List. So
this little demonstration shows that for expressions really are equivalent in
their expressiveness to applications of the three functions map, flatMap, and
withFilter.
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23.6
Generalizing for
Because the translation of for expressions only relies on the presence of
methods map, flatMap, and withFilter, it is possible to apply the for
notation to a large class of data types.
You have already seen for expressions over lists and arrays. These are
supported because lists, as well as arrays, define operations map, flatMap,
and withFilter. Because they define a foreach method as well, for loops
over these data types are also possible.
Besides lists and arrays, there are many other types in the Scala standard
library that support the same four methods and therefore allow for expressions. Examples are ranges, iterators, streams, and all implementations of
sets. It’s also perfectly possible for your own data types to support for expressions by defining the necessary methods. To support the full range of for
expressions and for loops, you need to define map, flatMap, withFilter,
and foreach as methods of your data type. But it’s also possible to define
a subset of these methods, and thereby support a subset of all possible for
expressions or loops.
Here are the precise rules:
• If your type defines just map, it allows for expressions consisting of a
single generator.
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• If it defines flatMap as well as map, it allows for expressions consisting of several generators.
• If it defines foreach, it allows for loops (both with single and multiple generators).
• If it defines withFilter, it allows for filter expressions starting with
an if in the for expression.
The translation of for expressions happens before type checking. This allows for maximum flexibility because the only requirement is that the result
of expanding a for expression type checks. Scala defines no typing rules
for the for expressions themselves, and does not require that methods map,
flatMap, withFilter, or foreach have any particular type signatures.
Nevertheless, there is a typical setup that captures the most common
intention of the higher order methods to which for expressions translate.
Say you have a parameterized class, C, which typically would stand for some
sort of collection. Then it’s quite natural to pick the following type signatures
for map, flatMap, withFilter, and foreach:
abstract class C[A] {
def map[B](f: A => B): C[B]
def flatMap[B](f: A => C[B]): C[B]
def withFilter(p: A => Boolean): C[A]
def foreach(b: A => Unit): Unit
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}
That is, the map function takes a function from the collection’s element type
A to some other type B. It produces a new collection of the same kind C, but
with B as the element type. The flatMap method takes a function f from A to
some C-collection of Bs and produces a C-collection of Bs. The withFilter
method takes a predicate function from the collection’s element type A to
Boolean. It produces a collection of the same type as the one on which it is
invoked. Finally, the foreach method takes a function from A to Unit and
produces a Unit result:
In class C above, the withFilter method produces a new collection of
the same class. That means that every invocation of withFilter creates a
new C object, just the same as filter would work. Now, in the translation
of for expressions, any calls to withFilter are always followed by calls to
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one of the other three methods. Therefore, the object created by withFilter
will be taken apart by one of the other methods immediately afterwards. If
objects of class C are large (think long sequences), you might want to avoid
the creation of such an intermediate object. A standard technique is to let
withFilter return not a C object but just a wrapper object that “remembers”
that elements need to be filtered before being processed further.
Concentrating on just the first three functions of class C, the following
facts are noteworthy. In functional programming, there’s a general concept
called a monad, which can explain a large number of types with computations, ranging from collections, to computations with state and I/O, backtracking computations, and transactions, to name a few. You can formulate
functions map, flatMap, and withFilter on a monad, and, if you do, they
end up having exactly the types given here.
Furthermore, you can characterize every monad by map, flatMap, and
withFilter, plus a “unit” constructor that produces a monad from an element value. In an object-oriented language, this “unit” constructor is simply
an instance constructor or a factory method. Therefore, map, flatMap, and
withFilter can be seen as an object-oriented version of the functional concept of monad. Because for expressions are equivalent to applications of
these three methods, they can be seen as syntax for monads.
All this suggests that the concept of for expression is more general than
just iteration over a collection, and indeed it is. For instance, for expressions
also play an important role in asynchronous I/O, or as an alternative notation
for optional values. Watch out in the Scala libraries for occurrences of map,
flatMap, and withFilter—when they are present, for expressions suggest
themselves as a concise way of manipulating elements of the type.
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23.7
Conclusion
In this chapter, you were given a peek under the hood of for expressions and
for loops. You learned that they translate into applications of a standard set
of higher-order methods. As a result, you saw that for expressions are really
much more general than mere iterations over collections, and that you can
design your own classes to support them.
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Chapter 24
Collections in Depth
Scala includes an elegant and powerful collection library. Even though the
collections API is subtle at first glance, the changes it can provoke in your
programming style can be profound. Quite often it’s as if you work on a
higher level with the basic building blocks of a program being whole collections instead of their elements. This new style of programming requires some
adaptation. Fortunately, the adaptation is helped by several nice properties
of Scala collections. They are easy to use, concise, safe, fast, and universal.
Easy to use: A small vocabulary of twenty to fifty methods is enough to
solve most collection problems in a couple of operations. No need
to wrap your head around complicated looping structures or recursions. Persistent collections and side-effect-free operations mean that
you need not worry about accidentally corrupting existing collections
with new data. Interference between iterators and collection updates
is eliminated.
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Concise: You can achieve with a single word what used to take one or several loops. You can express functional operations with lightweight
syntax and combine operations effortlessly, so that the result feels like
a custom algebra.
Safe: This one has to be experienced to sink in. The statically typed and
functional nature of Scala’s collections means that the overwhelming
majority of errors you might make are caught at compile-time. The
reason is that (1) the collection operations themselves are heavily used
and therefore well tested. (2) the usages of the collection operation
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Chapter 24 · Collections in Depth
make inputs and output explicit as function parameters and results. (3)
These explicit inputs and outputs are subject to static type checking.
The bottom line is that the large majority of misuses will manifest
themselves as type errors. It’s not at all uncommon to have programs
of several hundred lines run at first try.
Fast: Collection operations are tuned and optimized in the libraries. As a result, using collections is typically quite efficient. You might be able to
do a little bit better with carefully hand-tuned data structures and operations, but you might also do a lot worse by making some suboptimal
implementation decisions along the way. What’s more, collections are
have been adapted to parallel execution on multi-cores. Parallel collections support the same operations as sequential ones, so no new
operations need to be learned and no code needs to be rewritten. You
can turn a sequential collection into a parallel one simply by invoking
the par method.
Universal: Collections provide the same operations on any type where it
makes sense to do so. So you can achieve a lot with a fairly small
vocabulary of operations. For instance, a string is conceptually a sequence of characters. Consequently, in Scala collections, strings support all sequence operations. The same holds for arrays.
This chapter describes in depth the APIs of the Scala collection classes
from a user perspective. You’ve already seen a quick tour of the collections
library, in Chapter 17. This chapter takes you on a more detailed tour, showing all the collection classes and all the methods they define, so it includes
everything you need to know to use Scala collections. Looking ahead, Chapter 25 will concentrate on the architecture and extensibility aspects of the
library, for people implementing new collection types.
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24.1
Mutable and immutable collections
As is now familiar to you, Scala collections systematically distinguish between mutable and immutable collections. A mutable collection can be updated or extended in place. This means you can change, add, or remove
elements of a collection as a side effect. Immutable collections, by contrast,
never change. You still have operations that simulate additions, removals, or
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updates, but those operations will in each case return a new collection and
leave the old collection unchanged.
All collection classes are found in the package scala.collection or
one of its subpackages: mutable, immutable, and generic. Most collection classes needed by client code exist in three variants, each of which has
different characteristics with respect to mutability. The three variants are
located in packages scala.collection, scala.collection.immutable,
and scala.collection.mutable.
A collection in package scala.collection.immutable is guaranteed
to be immutable for everyone. Such a collection will never change after
it is created. Therefore, you can rely on the fact that accessing the same
collection value repeatedly at different points in time will always yield a
collection with the same elements.
A collection in package scala.collection.mutable is known to have
some operations that change the collection in place. These operations let you
write code to mutate the collection yourself. However, you must be careful
to understand and defend against any updates performed by other parts of
the code base.
A collection in package scala.collection can be either mutable or immutable. For instance, scala.collection.IndexedSeq[T] is a supertrait
of both scala.collection.immutable.IndexedSeq[T] and its mutable
sibling scala.collection.mutable.IndexedSeq[T]. Generally, the root
collections in package scala.collection define the same interface as the
immutable collections. And typically, the mutable collections in package
scala.collection.mutable add some side-effecting modification operations to this immutable interface.
The difference between root collections and immutable collections is that
clients of an immutable collection have a guarantee that nobody can mutate
the collection, whereas clients of a root collection only know that they can’t
change the collection themselves. Even though the static type of such a
collection provides no operations for modifying the collection, it might still
be possible that the run-time type is a mutable collection that can be changed
by other clients.
By default, Scala always picks immutable collections. For instance, if
you just write Set without any prefix or without having imported anything,
you get an immutable set, and if you write Iterable you get an immutable
iterable, because these are the default bindings imported from the scala
package. To get the mutable default versions, you need to write explicitly
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collection.mutable.Set, or collection.mutable.Iterable.
The last package in the collection hierarchy is collection.generic.
This package contains building blocks for implementing collections. Typically, collection classes defer the implementations of some of their operations to classes in generic. Everyday users of the collection framework on
the other hand should need to refer to classes in generic only in exceptional
circumstances.
24.2
Collections consistency
The most important collection classes are shown in Figure 24.1. There is
quite a bit of commonality shared by all these classes. For instance, every
kind of collection can be created by the same uniform syntax, writing the
collection class name followed by its elements:
Traversable(1, 2, 3)
Iterable("x", "y", "z")
Map("x" -> 24, "y" -> 25, "z" -> 26)
Set(Color.Red, Color.Green, Color.Blue)
SortedSet("hello", "world")
Buffer(x, y, z)
IndexedSeq(1.0, 2.0)
LinearSeq(a, b, c)
The same principle also applies for specific collection implementations:
List(1, 2, 3)
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HashMap("x" -> 24, "y" -> 25, "z" -> 26)
The toString methods for all collections produce output written as above,
with a type name followed by the elements of the collection in parentheses.
All collections support the API provided by Traversable, but their methods all return their own class rather than the root class Traversable. For
instance, the map method on List has a return type of List, whereas the map
method on Set has a return type of Set. Thus the static return type of these
methods is fairly precise:
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Traversable
Iterable
Seq
IndexedSeq
Vector
ResizableArray
GenericArray
LinearSeq
MutableList
List
Stream
Buffer
ListBuffer
ArrayBuffer
Set
SortedSet
TreeSet
HashSet (mutable)
LinkedHashSet
HashSet (immutable)
BitSet
EmptySet, Set1, Set2, Set3, Set4
Map
SortedMap
TreeMap
HashMap (mutable)
LinkedHashMap (mutable)
HashMap (immutable)
EmptyMap, Map1, Map2, Map3, Map4
Figure 24.1 · Collection hierarchy.
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scala> List(1, 2, 3) map (_ + 1)
res0: List[Int] = List(2, 3, 4)
scala> Set(1, 2, 3) map (_ * 2)
res1: scala.collection.immutable.Set[Int] = Set(2, 4, 6)
Equality is also organized uniformly for all collection classes; more on this
in Section 24.13.
Most of the classes in Figure 24.1 exist in three variants: root, mutable,
and immutable. The only exception is the Buffer trait, which only exists as
a mutable collection.
In the remainder of this chapter, we will review these classes one by one.
24.3
Trait Traversable
At the top of the collection hierarchy is trait Traversable. Its only abstract
operation is foreach:
def foreach[U](f: Elem => U)
Collection classes implementing Traversable just need to define this
method; all other methods can be inherited from Traversable.
The foreach method is meant to traverse all elements of the collection,
and apply the given operation, f, to each element. The type of the operation
is Elem => U, where Elem is the type of the collection’s elements and U is an
arbitrary result type. The invocation of f is done for its side effect only; in
fact any function result of f is discarded by foreach.
Traversable also defines many concrete methods, which are all listed
in Table 24.1 on page 535. These methods fall into the following categories:
Addition ++, which appends two traversables together, or appends all elements of an iterator to a traversable.
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Map operations map, flatMap, and collect, which produce a new collection by applying some function to collection elements.
Conversions toIndexedSeq, toIterable, toStream, toArray, toList,
toSeq, toSet, and toMap, which turn a Traversable collection into a
more specific collection. All these conversions return the receiver object if it already matches the demanded collection type. For instance,
applying toList to a list will yield the list itself.
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Copying operations copyToBuffer and copyToArray. As their names imply, these copy collection elements to a buffer or array, respectively.
Size operations isEmpty, nonEmpty, size, and hasDefiniteSize. Collections that are traversable can be finite or infinite. An example
of an infinite traversable collection is the stream of natural numbers
Stream.from(0). The method hasDefiniteSize indicates whether
a collection is possibly infinite. If hasDefiniteSize returns true, the
collection is certainly finite. If it returns false, the collection might be
infinite, in which case size will emit an error or not return.
Element retrieval operations head, last, headOption, lastOption, and
find. These select the first or last element of a collection, or else the
first element matching a condition. Note, however, that not all collections have a well-defined meaning of what “first” and “last” means.
For instance, a hash set might store elements according to their hash
keys, which might change from run to run. In that case, the “first”
element of a hash set could also be different for different runs of a
program. A collection is ordered if it always yields its elements in the
same order. Most collections are ordered, but some (such as hash sets)
are not—dropping the ordering provides a little bit of extra efficiency.
Ordering is often essential to give reproducible tests and help in debugging. That’s why Scala collections provide ordered alternatives for
all collection types. For instance, the ordered alternative for HashSet
is LinkedHashSet.
Subcollection retrieval operations takeWhile, tail, init, slice, take,
drop, filter, dropWhile, filterNot, and withFilter. These all
return some subcollection identified by an index range or a predicate.
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Subdivision operations splitAt, span, partition, and groupBy, which
split the elements of this collection into several subcollections.
Element tests exists, forall, and count, which test collection elements
with a given predicate.
Folds foldLeft, foldRight, /:, :\, reduceLeft, reduceRight, which
apply a binary operation to successive elements.
Specific folds sum, product, min, and max, which work on collections of
specific types (numeric or comparable).
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String operations mkString, addString, and stringPrefix, which provide alternative ways of converting a collection to a string.
View operations consisting of two overloaded variants of the view method.
A view is a collection that’s evaluated lazily. You’ll learn more about
views in Section 24.14.
Table 24.1 · Operations in trait Traversable
What it is
What it does
Abstract method:
xs foreach f
Executes function f for every element of xs.
Addition:
xs ++ ys
A collection consisting of the elements of both xs
and ys. ys is a TraversableOnce collection, i.e.,
either a Traversable or an Iterator.
xs map f
The collection obtained from applying the
function f to every element in xs.
Maps:
xs flatMap f
The collection obtained from applying the
collection-valued function f to every element in
xs and concatenating the results.
xs collect f
The collection obtained from applying the partial
function f to every element in xs for which it is
defined and collecting the results.
Conversions:
xs.toArray
xs.toList
Converts the collection to a list.
xs.toIterable
Converts the collection to an iterable.
xs.toSeq
Converts the collection to a sequence.
xs.toIndexedSeq
Prepared for Library Genesis
Converts the collection to an array.
xs.toStream
Converts the collection to an indexed sequence.
Converts the collection to a stream (a lazily
computed sequence).
xs.toSet
Converts the collection to a set.
xs.toMap
Converts a collection of key/value pairs to a map.
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Table 24.1 · continued
Copying:
xs copyToBuffer buf
xs copyToArray(arr, s, len)
Copies all elements of the collection to buffer
buf.
Copies at most len elements of arr, starting at
index s. The last two arguments are optional.
Size info:
xs.isEmpty
Tests whether the collection is empty.
xs.nonEmpty
Tests whether the collection contains elements.
xs.size
xs.hasDefiniteSize
The number of elements in the collection.
True if xs is known to have finite size.
Element retrieval:
xs.head
xs.headOption
The first element of the collection (or, some
element, if no order is defined).
The first element of xs in an option value, or
None if xs is empty.
xs.last
xs.lastOption
xs find p
The last element of the collection (or, some
element, if no order is defined).
The last element of xs in an option value, or None
if xs is empty.
An option containing the first element in xs that
satisfies p, or None if no element qualifies.
Subcollections:
xs.tail
The rest of the collection except xs.head.
xs.init
The rest of the collection except xs.last.
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xs slice (from, to)
A collection consisting of elements in some index
range of xs (from from, up to and excluding to).
xs take n
A collection consisting of the first n elements of
xs (or, some arbitrary n elements, if no order is
defined).
xs drop n
The rest of the collection except xs take n.
xs takeWhile p
The longest prefix of elements in the collection
that all satisfy p.
xs dropWhile p
The collection without the longest prefix of
elements that all satisfy p.
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Table 24.1 · continued
xs filter p
The collection consisting of those elements of xs
that satisfy the predicate p.
xs withFilter p
A non-strict filter of this collection. All
operations on the resulting filter will only apply
to those elements of xs for which the condition p
is true.
xs filterNot p
The collection consisting of those elements of xs
that do not satisfy the predicate p.
Subdivisions:
xs splitAt n
xs span p
xs partition p
xs groupBy f
Splits xs at a position, giving the pair of
collections (xs take n, xs drop n).
Splits xs according to a predicate, giving the pair
of collections (xs takeWhile p,
xs.dropWhile p).
Splits xs into a pair of collections; one with
elements that satisfy the predicate p, the other
with elements that do not, giving the pair of
collections (xs filter p, xs.filterNot p).
Partitions xs into a map of collections according
to a discriminator function f.
Element conditions:
xs forall p
A boolean indicating whether the predicate p
holds for all elements of xs.
xs exists p
A boolean indicating whether the predicate p
holds for some element in xs.
xs count p
The number of elements in xs that satisfy the
predicate p.
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Folds:
(z /: xs)(op)
Applies binary operation op between successive
elements of xs, going left to right, starting with z.
(xs :\ z)(op)
Applies binary operation op between successive
elements of xs, going right to left, starting with z.
xs.foldLeft(z)(op)
Same as (z /: xs)(op).
xs.foldRight(z)(op)
Same as (xs :\ z)(op).
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Table 24.1 · continued
xs reduceLeft op
Applies binary operation op between successive
elements of non-empty collection xs, going left
to right.
xs reduceRight op
Applies binary operation op between successive
elements of non-empty collection xs, going right
to left.
Specific folds:
xs.sum
xs.product
The sum of the numeric element values of
collection xs.
The product of the numeric element values of
collection xs.
xs.min
The minimum of the ordered element values of
collection xs.
xs.max
The maximum of the ordered element values of
collection xs.
Strings:
xs addString (b, start,
sep, end)
Adds a string to StringBuilder b that shows all
elements of xs between separators sep enclosed
in strings start and end. start, sep, and end
are all optional.
xs mkString (start,
sep, end)
Converts the collection to a string that shows all
elements of xs between separators sep enclosed
in strings start and end. start, sep, and end
are all optional.
xs.stringPrefix
The collection name at the beginning of the string
returned from xs.toString.
Views:
xs.view
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xs view (from, to)
24.4
Produces a view over xs.
Produces a view that represents the elements in
some index range of xs.
Trait Iterable
The next trait from the top in Figure 24.1 is Iterable. All methods in this
trait are defined in terms of an abstract method, iterator, which yields the
collection’s elements one by one. The abstract foreach method inherited
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from trait Traversable is implemented in Iterable in terms of iterator.
Here is the actual implementation:
def foreach[U](f: Elem => U): Unit = {
val it = iterator
while (it.hasNext) f(it.next())
}
Quite a few subclasses of Iterable override this standard implementation
of foreach in Iterable, because they can provide a more efficient implementation. Remember that foreach is the basis of the implementation of all
operations in Traversable, so its performance matters.
Two more methods exist in Iterable that return iterators: grouped and
sliding. These iterators, however, do not return single elements but whole
subsequences of elements of the original collection. The maximal size of
these subsequences is given as an argument to these methods. The grouped
method chunks its elements into increments, whereas sliding yields a sliding window over the elements. The difference between the two should become clear by looking at the following interpreter interaction:
scala> val xs = List(1, 2, 3, 4, 5)
xs: List[Int] = List(1, 2, 3, 4, 5)
scala> val git = xs grouped 3
git: Iterator[List[Int]] = non-empty iterator
scala> git.next()
res2: List[Int] = List(1, 2, 3)
scala> git.next()
res3: List[Int] = List(4, 5)
scala> val sit = xs sliding 3
sit: Iterator[List[Int]] = non-empty iterator
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scala> sit.next()
res4: List[Int] = List(1, 2, 3)
scala> sit.next()
res5: List[Int] = List(2, 3, 4)
scala> sit.next()
res6: List[Int] = List(3, 4, 5)
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Trait Iterable also adds some other methods to Traversable that can be
implemented efficiently only if an iterator is available. They are summarized
in Table 24.2:
Table 24.2 · Operations in trait Iterable
What it is
What it does
Abstract method:
xs.iterator
An iterator that yields every element in xs, in the
same order as foreach traverses elements
Other iterators:
xs grouped size
An iterator that yields fixed-sized “chunks” of
this collection
xs sliding size
An iterator that yields a sliding fixed-sized
window of elements in this collection
Subcollections:
xs takeRight n
A collection consisting of the last n elements of
xs (or, some arbitrary n elements, if no order is
defined)
xs dropRight n
The rest of the collection except xs takeRight n
Zippers:
xs zip ys
An iterable of pairs of corresponding elements
from xs and ys
xs zipAll (ys, x, y)
An iterable of pairs of corresponding elements
from xs and ys, where the shorter sequence is
extended to match the longer one by appending
elements x or y
xs.zipWithIndex
An iterable of pairs of elements from xs with
their indicies
Comparison:
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xs sameElements ys
Tests whether xs and ys contain the same
elements in the same order
Why have both Traversable and Iterable?
You might wonder why the extra trait Traversable is above Iterable.
Can we not do everything with an iterator? So what’s the point of having
a more abstract trait that defines its methods in terms of foreach instead
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of iterator? One reason for having Traversable is that sometimes it is
easier or more efficient to provide an implementation of foreach than to
provide an implementation of iterator. Here’s a simple example. Let’s
say you want a class hierarchy for binary trees that have integer elements at
the leaves. You might design this hierarchy like this:
sealed abstract class Tree
case class Branch(left: Tree, right: Tree) extends Tree
case class Node(elem: Int) extends Tree
Now assume you want to make trees traversable. To do this, have Tree
inherit from Traversable[Int] and define a foreach method like this:
sealed abstract class Tree extends Traversable[Int] {
def foreach[U](f: Int => U) = this match {
case Node(elem) => f(elem)
case Branch(l, r) => l foreach f; r foreach f
}
}
That’s not too hard, and it is also very efficient—traversing a balanced tree
takes time proportional to the number of elements in the tree. To see this,
consider that for a balanced tree with N leaves you will have N - 1 interior
nodes of class Branch. So the total number of steps to traverse the tree is
N + N - 1.
Now, compare this with making trees iterable. To do this, have Tree
inherit from Iterable[Int] and define an iterator method like this:
sealed abstract class Tree extends Iterable[Int] {
def iterator: Iterator[Int] = this match {
case Node(elem) => Iterator.single(elem)
case Branch(l, r) => l.iterator ++ r.iterator
}
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}
At first glance, this looks no harder than the foreach solution. However,
there’s an efficiency problem that has to do with the implementation of the
iterator concatenation method, ++. Every time an element is produced by a
concatenated iterator such as l.iterator ++ r.iterator, the computation
needs to follow one indirection to get at the right iterator (either l.iterator,
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or r.iterator). Overall, that makes log(N) indirections to get at a leaf of
a balanced tree with N leaves. So the cost of visiting all elements of a tree
went up from about 2N for the foreach traversal method to N log(N) for the
traversal with iterator. If the tree has a million elements that means about
two million steps for foreach and about twenty million steps for iterator.
So the foreach solution has a clear advantage.
Subcategories of Iterable
In the inheritance hierarchy below Iterable you find three traits: Seq, Set,
and Map. A common aspect of these three traits is that they all implement the
PartialFunction trait1 with its apply and isDefinedAt methods. However, the way each trait implements PartialFunction differs.
For sequences, apply is positional indexing, where elements are always
numbered from 0. That is, Seq(1, 2, 3)(1) == 2. For sets, apply is a
membership test. For instance, Set('a', 'b', 'c')('b') == true whereas
Set()('a') == false. Finally for maps, apply is a selection. For instance,
Map('a' -> 1, 'b' -> 10, 'c' -> 100)('b') == 10.
In the following three sections, we will explain each of the three kinds of
collections in more detail.
24.5
The sequence traits Seq, IndexedSeq, and
LinearSeq
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The Seq trait represents sequences. A sequence is a kind of iterable that has
a length and whose elements have fixed index positions, starting from 0.
The operations on sequences, summarized in Figure 24.3, fall into the
following categories:
Indexing and length operations apply, isDefinedAt, length, indices,
and lengthCompare. For a Seq, the apply operation means indexing; hence a sequence of type Seq[T] is a partial function that takes
an Int argument (an index) and yields a sequence element of type T.
In other words Seq[T] extends PartialFunction[Int, T]. The elements of a sequence are indexed from zero up to the length of the
sequence minus one. The length method on sequences is an alias of
1 Partial
functions were described in Section 15.7.
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the size method of general collections. The lengthCompare method
allows you to compare the lengths of two sequences even if one of the
sequences has infinite length.
Index search operations indexOf, lastIndexOf, indexOfSlice, lastIndexOfSlice, indexWhere, lastIndexWhere, segmentLength, and
prefixLength, which return the index of an element equal to a given
value or matching some predicate.
Addition operations +:, :+, and padTo, which return new sequences obtained by adding elements at the front or the end of a sequence.
Update operations updated and patch, which return a new sequence obtained by replacing some elements of the original sequence.
Sorting operations sorted, sortWith, and sortBy, which sort sequence
elements according to various criteria.
Reversal operations reverse, reverseIterator, and reverseMap, which
yield or process sequence elements in reverse order, from last to first.
Comparison operations startsWith, endsWith, contains, corresponds,
and containsSlice, which relate two sequences or search an element
in a sequence.
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Multiset operations intersect, diff, union, and distinct, which perform set-like operations on the elements of two sequences or remove
duplicates.
If a sequence is mutable, it offers in addition a side-effecting update method,
which lets sequence elements be updated. Recall from Chapter 3 that syntax
like seq(idx) = elem is just a shorthand for seq.update(idx, elem). Note
the difference between update and updated. The update method changes
a sequence element in place, and is only available for mutable sequences.
The updated method is available for all sequences and always returns a new
sequence instead of modifying the original.
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Table 24.3 · Operations in trait Seq
What it is
What it does
Indexing and length:
xs(i)
xs isDefinedAt i
xs.length
xs.lengthCompare ys
xs.indices
(or, written out, xs apply i) The element of xs at
index i.
Tests whether i is contained in xs.indices.
The length of the sequence (same as size).
Returns -1 if xs is shorter than ys, +1 if it is
longer, and 0 is they have the same length. Works
even if one of the sequences is infinite.
The index range of xs, extending from 0 to
xs.length - 1.
Index search:
xs indexOf x
The index of the first element in xs equal to x
(several variants exist).
xs lastIndexOf x
The index of the last element in xs equal to x
(several variants exist).
xs indexOfSlice ys
The first index of xs such that successive
elements starting from that index form the
sequence ys.
xs lastIndexOfSlice ys
The last index of xs such that successive elements
starting from that index form the sequence ys.
xs indexWhere p
The index of the first element in xs that satisfies p
(several variants exist).
xs segmentLength (p, i)
xs prefixLength p
The length of the longest uninterrupted segment
of elements in xs, starting with xs(i), that all
satisfy the predicate p.
The length of the longest prefix of elements in xs
that all satisfy the predicate p.
Prepared for Library Genesis
Additions:
x +: xs
A new sequence consisting of x prepended to xs.
xs :+ x
A new sequence that consists of x append to xs.
xs padTo (len, x)
The sequence resulting from appending the value
x to xs until length len is reached.
Updates:
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Table 24.3 · continued
xs patch (i, ys, r)
The sequence resulting from replacing r elements
of xs starting with i by the patch ys.
xs updated (i, x)
A copy of xs with the element at index i replaced
by x.
xs(i) = x
(or, written out, xs.update(i, x), only available
for mutable.Seqs) Changes the element of xs at
index i to y.
xs.sorted
A new sequence obtained by sorting the elements
of xs using the standard ordering of the element
type of xs.
xs sortWith lessThan
A new sequence obtained by sorting the elements
of xs, using lessThan as comparison operation.
xs sortBy f
A new sequence obtained by sorting the elements
of xs. Comparison between two elements
proceeds by mapping the function f over both
and comparing the results.
Sorting:
Reversals:
xs.reverse
A sequence with the elements of xs in reverse
order.
xs.reverseIterator
An iterator yielding all the elements of xs in
reverse order.
xs reverseMap f
A sequence obtained by mapping f over the
elements of xs in reverse order.
Comparisons:
xs startsWith ys
Tests whether xs starts with sequence ys (several
variants exist).
xs endsWith ys
Tests whether xs ends with sequence ys (several
variants exist).
xs contains x
Tests whether xs has an element equal to x.
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xs containsSlice ys
(xs corresponds ys)(p)
Tests whether xs has a contiguous subsequence
equal to ys.
Tests whether corresponding elements of xs and
ys satisfy the binary predicate p.
Multiset operations:
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Table 24.3 · continued
xs intersect ys
The multi-set intersection of sequences xs and ys
that preserves the order of elements in xs.
xs diff ys
The multi-set difference of sequences xs and ys
that preserves the order of elements in xs.
xs union ys
Multiset union; same as xs ++ ys.
xs.distinct
A subsequence of xs that contains no duplicated
element.
Each Seq trait has two subtraits, LinearSeq and IndexedSeq. These do
not add any new operations, but each offers different performance characteristics. A linear sequence has efficient head and tail operations, whereas
an indexed sequence has efficient apply, length, and (if mutable) update
operations. List is a frequently used linear sequence, as is Stream. Two frequently used indexed sequences are Array and ArrayBuffer. The Vector
class provides an interesting compromise between indexed and linear access. It has both effectively constant time indexing overhead and constant
time linear access overhead. Because of this, vectors are a good foundation
for mixed access patterns where both indexed and linear accesses are used.
More on vectors in Section 24.8.
Prepared for Library Genesis
Buffers
An important sub-category of mutable sequences is buffers. Buffers allow
not only updates of existing elements but also element insertions, element
removals, and efficient additions of new elements at the end of the buffer.
The principal new methods supported by a buffer are += and ++=, for element
addition at the end, +=: and ++=: for addition at the front, insert and
insertAll for element insertions, as well as remove and -= for element
removal. These operations are summarized in Table 24.4.
Two Buffer implementations that are commonly used are ListBuffer
and ArrayBuffer. As the name implies, a ListBuffer is backed by a
List and supports efficient conversion of its elements to a List, whereas an
ArrayBuffer is backed by an array, and can be quickly converted into one.
You saw a glimpse of the implementation of ListBuffer in Section 22.2.
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Table 24.4 · Operations in trait Buffer
What it is
What it does
Additions:
buf += x
buf += (x, y, z)
buf ++= xs
x +=: buf
Appends element x to buffer buf, and returns buf
itself as result
Appends given elements to buffer
Appends all elements in xs to buffer
Prepends element x to buffer
xs ++=: buf
Prepends all elements in xs to buffer
buf insert (i, x)
Inserts element x at index i in buffer
buf insertAll (i, xs)
Inserts all elements in xs at index i in buffer
Removals:
buf -= x
buf remove i
buf remove (i, n)
Removes element x from buffer
Removes element at index i from buffer
Removes n elements starting at index i from
buffer
buf trimStart n
Removes first n elements from buffer
buf trimEnd n
Removes last n elements from buffer
buf.clear()
Removes all elements from buffer
Cloning:
buf.clone
24.6
A new buffer with the same elements as buf
Sets
Sets are Iterables that contain no duplicate elements. The operations on
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sets are summarized in Table 24.5 for general sets and Table 24.6 for mutable
sets. They fall into the following categories:
Tests contains, apply, and subsetOf. The contains method indicates
whether a set contains a given element. The apply method for a set
is the same as contains, so set(elem) is the same as set contains
elem. That means sets can also be used as test functions that return
true for the elements they contain. For example:
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scala> val fruit = Set("apple", "orange", "peach", "banana")
fruit: scala.collection.immutable.Set[String] =
Set(apple, orange, peach, banana)
scala> fruit("peach")
res7: Boolean = true
scala> fruit("potato")
res8: Boolean = false
Additions + and ++, which add one or more elements to a set, yielding a new
set as a result.
Removals - and --, which remove one or more elements from a set, yielding
a new set.
Set operations for union, intersection, and set difference. These set operations exist in two forms: alphabetic and symbolic. The alphabetic
versions are intersect, union, and diff, whereas the symbolic versions are &, |, and &~. The ++ that Set inherits from Traversable
can be seen as yet another alias of union or |, except that ++ takes a
Traversable argument whereas union and | take sets.
Table 24.5 · Operations in trait Set
What it is
What it does
Tests:
xs contains x
xs(x)
xs subsetOf ys
Tests whether x is an element of xs
Same as xs contains x
Tests whether xs is a subset of ys
Additions:
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xs + x
The set containing all elements of xs as well as x
xs + (x, y, z)
The set containing all elements of xs as well as
the given additional elements
xs ++ ys
The set containing all elements of xs as well as
all elements of ys
Removals:
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Table 24.5 · continued
xs - x
The set containing all elements of xs except x
xs - (x, y, z)
The set containing all elements of xs except the
given elements
xs -- ys
The set containing all elements of xs except the
elements of ys
xs.empty
An empty set of the same class as xs
Binary operations:
xs & ys
xs intersect ys
xs | ys
xs union ys
xs &~ ys
xs diff ys
The set intersection of xs and ys
Same as xs & ys
The set union of xs and ys
Same as xs | ys
The set difference of xs and ys
Same as xs &~ ys
Mutable sets have methods that add, remove, or update elements, which
are summarized in Table 24.6:
Table 24.6 · Operations in trait mutable.Set
What it is
What it does
Additions:
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xs += x
Adds element x to set xs as a side effect and
returns xs itself
xs += (x, y, z)
Adds the given elements to set xs as a side effect
and returns xs itself
xs ++= ys
Adds all elements in ys to set xs as a side effect
and returns xs itself
xs add x
Adds element x to xs and returns true if x was
not previously contained in the set, false if it
was previously contained
xs -= x
Removes element x from set xs as a side effect
and returns xs itself
Removals:
xs -= (x, y, z)
Removes the given elements from set xs as a side
effect and returns xs itself
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Table 24.6 · continued
xs --= ys
Removes all elements in ys from set xs as a side
effect and returns xs itself
xs remove x
Removes element x from xs and returns true if x
was previously contained in the set, false if it
was not previously contained
xs retain p
Keeps only those elements in xs that satisfy
predicate p
xs.clear()
Removes all elements from xs
Update:
xs(x) = b
(or, written out, xs.update(x, b)) If boolean
argument b is true, adds x to xs, otherwise
removes x from xs
xs.clone
A new mutable set with the same elements as xs
Cloning:
Just like an immutable set, a mutable set offers the + and ++ operations
for element additions and the - and -- operations for element removals. But
these are less often used for mutable sets since they involve copying the set.
As a more efficient alternative, mutable sets offer the update methods += and
-=. The operation s += elem adds elem to the set s as a side effect, and
returns the mutated set as a result. Likewise, s -= elem removes elem from
the set, and returns the mutated set as a result. Besides += and -= there are
also the bulk operations ++= and --=, which add or remove all elements of a
traversable or an iterator.
The choice of the method names += and -= means that very similar code
can work with either mutable or immutable sets. Consider first the following
interpreter dialogue that uses an immutable set s:
scala> var s = Set(1, 2, 3)
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s: scala.collection.immutable.Set[Int] = Set(1, 2, 3)
scala> s += 4; s -= 2
scala> s
res10: scala.collection.immutable.Set[Int] = Set(1, 3, 4)
In this example, we used += and -= on a var of type immutable.Set. As
was explained in Step 10 in Chapter 3, a statement such as s += 4 is an
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abbreviation for s = s + 4. So this invokes the addition method + on the set s
and then assigns the result back to the s variable. Consider now an analogous
interaction with a mutable set:
scala> val s = collection.mutable.Set(1, 2, 3)
s: scala.collection.mutable.Set[Int] = Set(1, 2, 3)
scala> s += 4
res11: s.type = Set(1, 2, 3, 4)
scala> s -= 2
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res12: s.type = Set(1, 3, 4)
The end effect is very similar to the previous interaction; we start with a
Set(1, 2, 3) and end up with a Set(1, 3, 4). However, even though the
statements look the same as before, they do something different. The s += 4
statement now invokes the += method on the mutable set value s, changing
the set in place. Likewise, the s -= 2 statement now invokes the -= method
on the same set.
Comparing the two interactions shows an important principle. You often
can replace a mutable collection stored in a val by an immutable collection
stored in a var, and vice versa. This works at least as long as there are no
alias references to the collection through which you can observe whether it
was updated in place or a new collection was created.
Mutable sets also provide add and remove as variants of += and -=. The
difference is that add and remove return a boolean result indicating whether
the operation had an effect on the set.
The current default implementation of a mutable set uses a hash table
to store the set’s elements. The default implementation of an immutable set
uses a representation that adapts to the number of elements of the set. An
empty set is represented by just a singleton object. Sets of sizes up to four
are represented by a single object that stores all elements as fields. Beyond
that size, immutable sets are implemented as hash tries.2
A consequence of these representation choices is that for sets of small
sizes, up to about four, immutable sets are more compact and more efficient
than mutable sets. So if you expect the size of a set to be small, try to make
it immutable.
2 Hash
tries are described in Section 24.8.
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Section 24.7
24.7
Chapter 24 · Collections in Depth
Maps
Maps are Iterables of pairs of keys and values (also named mappings or associations). As explained in Section 21.4, Scala’s Predef class offers an implicit conversion that lets you write key -> value as an alternate syntax for
the pair (key, value). Therefore, Map("x" -> 24, "y" -> 25, "z" -> 26)
means exactly the same as Map(("x", 24), ("y", 25), ("z", 26)), but
reads better.
The fundamental operations on maps, summarized in Table 24.7, are
similar to those on sets. Mutable maps additionally support the operations
shown in Table 24.8. Map operations fall into the following categories:
Lookups apply, get, getOrElse, contains, and isDefinedAt. These operations turn maps into partial functions from keys to values. The fundamental lookup method for a map is:
def get(key): Option[Value]
The operation “m get key” tests whether the map contains an association for the given key. If so, it returns the associated value in a Some.
If no key is defined in the map, get returns None. Maps also define
an apply method that returns the value associated with a given key
directly, without wrapping it in an Option. If the key is not defined in
the map, an exception is raised.
Additions and updates +, ++, and updated, which let you add new bindings
to a map or change existing bindings.
Removals - and --, which remove bindings from a map.
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Subcollection producers keys, keySet, keysIterator, valuesIterator,
and values, which return a map’s keys and values separately in various forms.
Transformations filterKeys and mapValues, which produce a new map
by filtering and transforming bindings of an existing map.
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Table 24.7 · Operations in trait Map
What it is
What it does
Lookups:
ms get k
The value associated with key k in map ms as an
option, or None if not found
ms(k)
(or, written out, ms apply k) The value associated
with key k in map ms, or a thrown exception if not
found
ms getOrElse (k, d)
The value associated with key k in map ms, or the
default value d if not found
ms contains k
ms isDefinedAt k
Tests whether ms contains a mapping for key k
Same as contains
Additions and updates:
ms + (k -> v)
The map containing all mappings of ms as well as
the mapping k -> v from key k to value v
ms + (k -> v, l -> w)
The map containing all mappings of ms as well as
the given key/value pairs
ms ++ kvs
The map containing all mappings of ms as well as
all key/value pairs of kvs
ms updated (k, v)
Same as ms + (k -> v)
Removals:
ms - k
The map containing all mappings of ms except for
any mapping of key k
ms - (k, l, m)
The map containing all mappings of ms except for
any mapping with the given keys
ms -- ks
The map containing all mappings of ms except for
any mapping with a key in ks
Subcollections:
ms.keys
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ms.keySet
ms.keysIterator
An iterable containing each key in ms
A set containing each key in ms
An iterator yielding each key in ms
ms.values
An iterable containing each value associated with
a key in ms
ms.valuesIterator
An iterator yielding each value associated with a
key in ms
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Table 24.7 · continued
Transformation:
ms filterKeys p
ms mapValues f
A map view containing only those mappings in
ms where the key satisfies predicate p
A map view resulting from applying function f to
each value associated with a key in ms
Table 24.8 · Operations in trait mutable.Map
What it is
What it does
Additions and updates:
ms(k) = v
(or, written out, ms.update(k, v)) Adds
mapping from key k to value v to map ms as a side
effect, overwriting any previous mapping of k
ms += (k -> v)
Adds mapping from key k to value v to map ms as
a side effect and returns ms itself
ms += (k -> v, l -> w)
Adds the given mappings to ms as a side effect
and returns ms itself
ms ++= kvs
Adds all mappings in kvs to ms as a side effect
and returns ms itself
ms put (k, v)
Adds mapping from key k to value v to ms and
returns any value previously associated with k as
an option
ms getOrElseUpdate (k, d)
If key k is defined in map ms, returns its
associated value. Otherwise, updates ms with the
mapping k -> d and returns d
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Removals:
ms -= k
Removes mapping with key k from ms as a side
effect and returns ms itself
ms -= (k, l, m)
Removes mappings with the given keys from ms
as a side effect and returns ms itself
ms --= ks
Removes all keys in ks from ms as a side effect
and returns ms itself
ms remove k
Removes any mapping with key k from ms and
returns any value previously associated with k as
an option
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Table 24.8 · continued
ms retain p
Keeps only those mappings in ms that have a key
satisfying predicate p.
ms.clear()
Removes all mappings from ms
Transformation and cloning:
ms transform f
ms.clone
Transforms all associated values in map ms with
function f
Returns a new mutable map with the same
mappings as ms
The addition and removal operations for maps mirror those for sets. As
for sets, mutable maps also support the non-destructive addition operations
+, -, and updated, but they are used less frequently because they involve a
copying of the mutable map. Instead, a mutable map m is usually updated
“in place,” using the two variants m(key) = value or m += (key -> value).
There is also the variant m put (key, value), which returns an Option value
that contains the value previously associated with key, or None if the key did
not exist in the map before.
The getOrElseUpdate is useful for accessing maps that act as caches.
Say you have an expensive computation triggered by invoking a function f:
scala> def f(x: String) = {
println("taking my time."); Thread.sleep(100)
x.reverse }
f: (x: String)String
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Assume further that f has no side-effects, so invoking it again with the same
argument will always yield the same result. In that case you could save time
by storing previously computed bindings of argument and results of f in a
map, and only computing the result of f if a result of an argument was not
found there. You could say the map is a cache for the computations of the
function f.
scala> val cache = collection.mutable.Map[String, String]()
cache: scala.collection.mutable.Map[String,String] = Map()
You can now create a more efficient caching version of the f function:
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scala> def cachedF(s: String) = cache.getOrElseUpdate(s, f(s))
cachedF: (s: String)String
scala> cachedF("abc")
taking my time.
res16: String = cba
scala> cachedF("abc")
res17: String = cba
Note that the second argument to getOrElseUpdate is “by-name,” so the
computation of f("abc") above is only performed if getOrElseUpdate
requires the value of its second argument, which is precisely if its first argument is not found in the cache map. You could also have implemented
cachedF directly, using just basic map operations, but it would have have
taken more code to do so:
def cachedF(arg: String) = cache get arg match {
case Some(result) => result
case None =>
val result = f(arg)
cache(arg) = result
result
}
24.8
Concrete immutable collection classes
Scala provides many concrete immutable collection classes for you to choose
from. They differ in the traits they implement (maps, sets, sequences),
whether they can be infinite, and the speed of various operations. We’ll start
by reviewing the most common immutable collection types.
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Lists
Lists are finite immutable sequences. They provide constant-time access to
their first element as well as the rest of the list, and they have a constant-time
cons operation for adding a new element to the front of the list. Many other
operations take linear time. See Chapters 16 and 22 for extensive discussions
about lists.
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Streams
A stream is like a list except that its elements are computed lazily. Because
of this, a stream can be infinitely long. Only those elements requested will
be computed. Otherwise, streams have the same performance characteristics
as lists.
Whereas lists are constructed with the :: operator, streams are constructed with the similar-looking #::. Here is a simple example of a stream
containing the integers 1, 2, and 3:
scala> val str = 1 #:: 2 #:: 3 #:: Stream.empty
str: scala.collection.immutable.Stream[Int] = Stream(1, ?)
The head of this stream is 1, and the tail of it has 2 and 3. The tail is not
printed here, though, because it hasn’t been computed yet! Streams are required to compute lazily, and the toString method of a stream is careful not
to force any extra evaluation.
Below is a more complex example. It computes a stream that contains
a Fibonacci sequence starting with the given two numbers. A Fibonacci
sequence is one where each element is the sum of the previous two elements
in the series:
scala> def fibFrom(a: Int, b: Int): Stream[Int] =
a #:: fibFrom(b, a + b)
fibFrom: (a: Int, b: Int)Stream[Int]
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This function is deceptively simple. The first element of the sequence is
clearly a, and the rest of the sequence is the Fibonacci sequence starting
with b followed by a + b. The tricky part is computing this sequence without
causing an infinite recursion. If the function used :: instead of #::, then
every call to the function would result in another call, thus causing an infinite
recursion. Since it uses #::, though, the right-hand side is not evaluated until
it is requested.
Here are the first few elements of the Fibonacci sequence starting with
two ones:
scala> val fibs = fibFrom(1, 1).take(7)
fibs: scala.collection.immutable.Stream[Int] = Stream(1, ?)
scala> fibs.toList
res23: List[Int] = List(1, 1, 2, 3, 5, 8, 13)
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Vectors
Lists are very efficient when the algorithm processing them is careful to only
process their heads. Accessing, adding, and removing the head of a list takes
only constant time, whereas accessing or modifying elements later in the list
takes time linear in the depth into the list.
Vectors are a collection type that give efficient access to elements beyond
the head. Access to any elements of a vector take only “effectively constant
time,” as defined below. It’s a larger constant than for access to the head of a
list or for reading an element of an array, but it’s a constant nonetheless. As a
result, algorithms using vectors do not have to be careful about accessing just
the head of the sequence. They can access and modify elements at arbitrary
locations, and thus they can be much more convenient to write.
Vectors are built and modified just like any other sequence:
scala> val vec = scala.collection.immutable.Vector.empty
vec: scala.collection.immutable.Vector[Nothing] = Vector()
scala> val vec2 = vec :+ 1 :+ 2
vec2: scala.collection.immutable.Vector[Int] = Vector(1, 2)
scala> val vec3 = 100 +: vec2
vec3: scala.collection.immutable.Vector[Int]
= Vector(100, 1, 2)
scala> vec3(0)
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res24: Int = 100
Vectors are represented as broad, shallow trees. Every tree node contains
up to 32 elements of the vector or contains up to 32 other tree nodes. Vectors
with up to 32 elements can be represented in a single node. Vectors with
up to 32 * 32 = 1024 elements can be represented with a single indirection.
Two hops from the root of the tree to the final element node are sufficient
for vectors with up to 215 elements, three hops for vectors with 220 , four
hops for vectors with 225 elements and five hops for vectors with up to 230
elements. So for all vectors of reasonable size, an element selection involves
up to five primitive array selections. This is what we meant when we wrote
that element access is “effectively constant time.”
Vectors are immutable, so you cannot change an element of a vector in
place. However, with the updated method you can create a new vector that
differs from a given vector only in a single element:
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scala> val vec = Vector(1, 2, 3)
vec: scala.collection.immutable.Vector[Int] = Vector(1, 2, 3)
scala> vec updated (2, 4)
res25: scala.collection.immutable.Vector[Int] = Vector(1, 2, 4)
scala> vec
res26: scala.collection.immutable.Vector[Int] = Vector(1, 2, 3)
As the last line above shows, a call to updated has no effect on the original
vector vec. Like selection, functional vector updates are also “effectively
constant time.” Updating an element in the middle of a vector can be done by
copying the node that contains the element, and every node that points to it,
starting from the root of the tree. This means that a functional update creates
between one and five nodes that each contain up to 32 elements or subtrees.
This is certainly more expensive than an in-place update in a mutable array,
but still a lot cheaper than copying the whole vector.
Because vectors strike a good balance between fast random selections
and fast random functional updates, they are currently the default implementation of immutable indexed sequences:
scala> collection.immutable.IndexedSeq(1, 2, 3)
res27: scala.collection.immutable.IndexedSeq[Int]
= Vector(1, 2, 3)
Immutable stacks
If you need a last-in-first-out sequence, you can use a Stack. You push an
element onto a stack with push, pop an element with pop, and peek at the
top of the stack without removing it with top. All of these operations are
constant time.
Here are some simple operations performed on a stack:
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scala> val stack = scala.collection.immutable.Stack.empty
stack: scala.collection.immutable.Stack[Nothing] = Stack()
scala> val hasOne = stack.push(1)
hasOne: scala.collection.immutable.Stack[Int] = Stack(1)
scala> stack
res28: scala.collection.immutable.Stack[Nothing] = Stack()
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scala> hasOne.top
res29: Int = 1
scala> hasOne.pop
res30: scala.collection.immutable.Stack[Int] = Stack()
Immutable stacks are used rarely in Scala programs because their functionality is subsumed by lists: A push on an immutable stack is the same as
a :: on a list, and a pop on a stack is the same a tail on a list.
Immutable queues
A queue is just like a stack except that it is first-in-first-out rather than last-infirst-out. A simplified implementation of immutable queues was discussed
in Chapter 19. Here’s how you can create an empty immutable queue:
scala> val empty = scala.collection.immutable.Queue[Int]()
empty: scala.collection.immutable.Queue[Int] = Queue()
You can append an element to an immutable queue with enqueue:
scala> val has1 = empty.enqueue(1)
has1: scala.collection.immutable.Queue[Int] = Queue(1)
To append multiple elements to a queue, call enqueue with a collection as
its argument:
scala> val has123 = has1.enqueue(List(2, 3))
has123: scala.collection.immutable.Queue[Int] = Queue(1, 2,
3)
To remove an element from the head of the queue, use dequeue:
scala> val (element, has23) = has123.dequeue
element: Int = 1
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has23: scala.collection.immutable.Queue[Int] = Queue(2, 3)
Note that dequeue returns a pair consisting of the element removed and the
rest of the queue.
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Ranges
A range is an ordered sequence of integers that are equally spaced apart. For
example, “1, 2, 3” is a range, as is “5, 8, 11, 14.” To create a range in Scala,
use the predefined methods to and by. Here are some examples:
scala> 1 to 3
res31: scala.collection.immutable.Range.Inclusive
= Range(1, 2, 3)
scala> 5 to 14 by 3
res32: scala.collection.immutable.Range = Range(5, 8, 11, 14)
If you want to create a range that is exclusive of its upper limit, use the
convenience method until instead of to:
scala> 1 until 3
res33: scala.collection.immutable.Range = Range(1, 2)
Ranges are represented in constant space, because they can be defined
by just three numbers: their start, their end, and the stepping value. Because
of this representation, most operations on ranges are extremely fast.
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Hash tries
Hash tries3 are a standard way to implement immutable sets and maps efficiently. Their representation is similar to vectors in that they are also trees
where every node has 32 elements or 32 subtrees, but selection is done based
on a hash code. For instance, to find a given key in a map, you use the lowest
five bits of the hash code of the key to select the first subtree, the next five
bits the next subtree, and so on. Selection stops once all elements stored in a
node have hash codes that differ from each other in the bits that are selected
so far. Thus, not all the bits of the hash code are necessarily used.
Hash tries strike a nice balance between reasonably fast lookups and
reasonably efficient functional insertions (+) and deletions (-). That’s why
they underlie Scala’s default implementations of immutable maps and sets.
In fact, Scala has a further optimization for immutable sets and maps that
contain less than five elements. Sets and maps with one to four elements are
stored as single objects that just contain the elements (or key/value pairs in
3 “Trie”
comes from the word "retrieval" and is pronounced tree or try.
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the case of a map) as fields. The empty immutable set and empty immutable
map is in each case a singleton object—there’s no need to duplicate storage
for those because an empty immutable set or map will always stay empty.
Red-black trees
Red-black trees are a form of balanced binary trees where some nodes are
designated “red” and others “black.” Like any balanced binary tree, operations on them reliably complete in time logarithmic to the size of the tree.
Scala provides implementations of sets and maps that use a red-black
tree internally. You access them under the names TreeSet and TreeMap:
scala> val set = collection.immutable.TreeSet.empty[Int]
set: scala.collection.immutable.TreeSet[Int] = TreeSet()
scala> set + 1 + 3 + 3
res34: scala.collection.immutable.TreeSet[Int] = TreeSet(1, 3)
Red-black trees are also the standard implementation of SortedSet in Scala,
because they provide an efficient iterator that returns all elements of the set
in sorted order.
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Immutable bit sets
A bit set represents a collection of small integers as the bits of a larger integer.
For example, the bit set containing 3, 2, and 0 would be represented as the
integer 1101 in binary, which is 13 in decimal.
Internally, bit sets use an array of 64-bit Longs. The first Long in the
array is for integers 0 through 63, the second is for 64 through 127, and so
on. Thus, bit sets are very compact so long as the largest integer in the set is
less than a few hundred or so.
Operations on bit sets are very fast. Testing for inclusion takes constant
time. Adding an item to the set takes time proportional to the number of
Longs in the bit set’s array, which is typically a small number. Here are some
simple examples of the use of a bit set:
scala> val bits = scala.collection.immutable.BitSet.empty
bits: scala.collection.immutable.BitSet = BitSet()
scala> val moreBits = bits + 3 + 4 + 4
moreBits: scala.collection.immutable.BitSet = BitSet(3, 4)
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scala> moreBits(3)
res35: Boolean = true
scala> moreBits(0)
res36: Boolean = false
List maps
A list map represents a map as a linked list of key-value pairs. In general,
operations on a list map might have to iterate through the entire list. Thus,
operations on a list map take time linear in the size of the map. In fact there
is little usage for list maps in Scala because standard immutable maps are
almost always faster. The only possible difference is if the map is for some
reason constructed in such a way that the first elements in the list are selected
much more often than the other elements.
scala> val map = collection.immutable.ListMap(
1 -> "one", 2 -> "two")
map: scala.collection.immutable.ListMap[Int,String] = Map(1
-> one, 2 -> two)
scala> map(2)
res37: String = "two"
24.9
Concrete mutable collection classes
Now that you’ve seen the most commonly used immutable collection classes
that Scala provides in its standard library, take a look at the mutable collection classes.
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Array buffers
You’ve already seen array buffers in Section 17.1. An array buffer holds an
array and a size. Most operations on an array buffer have the same speed
as an array, because the operations simply access and modify the underlying
array. Additionally, array buffers can have data efficiently added to the end.
Appending an item to an array buffer takes amortized constant time. Thus,
array buffers are useful for efficiently building up a large collection whenever
the new items are always added to the end. Here are some examples:
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scala> val buf = collection.mutable.ArrayBuffer.empty[Int]
buf: scala.collection.mutable.ArrayBuffer[Int]
= ArrayBuffer()
scala> buf += 1
res38: buf.type = ArrayBuffer(1)
scala> buf += 10
res39: buf.type = ArrayBuffer(1, 10)
scala> buf.toArray
res40: Array[Int] = Array(1, 10)
List buffers
You’ve also already seen list buffers in Section 17.1. A list buffer is like an
array buffer except that it uses a linked list internally instead of an array. If
you plan to convert the buffer to a list once it is built up, use a list buffer
instead of an array buffer. Here’s an example:4
scala> val buf = collection.mutable.ListBuffer.empty[Int]
buf: scala.collection.mutable.ListBuffer[Int]
= ListBuffer()
scala> buf += 1
res41: buf.type = ListBuffer(1)
scala> buf += 10
res42: buf.type = ListBuffer(1, 10)
scala> buf.toList
res43: List[Int] = List(1, 10)
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String builders
Just like an array buffer is useful for building arrays, and a list buffer is
useful for building lists, a string builder is useful for building strings. String
builders are so commonly used that they are already imported into the default
namespace. Create them with a simple new StringBuilder, like this:
4 The “buf.type”
that appears in the interpreter responses in this and several other examples in this section is a singleton type. As will be explained in Section 29.6, buf.type
means the variable holds exactly the object referred to by buf.
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scala> val buf = new StringBuilder
buf: StringBuilder =
scala> buf += 'a'
res44: buf.type = a
scala> buf ++= "bcdef"
res45: buf.type = abcdef
scala> buf.toString
res46: String = abcdef
Linked lists
Linked lists are mutable sequences that consist of nodes that are linked with
next pointers. In most languages null would be picked as the empty linked
list. That does not work for Scala collections, because even empty sequences
must support all sequence methods. LinkedList.empty.isEmpty, in particular, should return true and not throw a NullPointerException. Empty
linked lists are encoded instead in a special way: Their next field points back
to the node itself.
Like their immutable cousins, linked lists are best operated on sequentially. In addition, linked lists make it easy to insert an element or linked list
into another linked list.
Double linked lists
DoubleLinkedLists are like the single linked lists described in the previous
subsection, except besides next, they have another mutable field, prev, that
points to the element preceding the current node. The main benefit of that
additional link is that it makes element removal very fast.
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Mutable lists
A MutableList consists of a single linked list together with a pointer that
refers to the terminal empty node of that list. This makes list append a constant time operation because it avoids having to traverse the list in search for
its terminal node. MutableList is currently the standard implementation of
mutable.LinearSeq in Scala.
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Queues
Scala provides mutable queues in addition to immutable ones. You use a
mutable queue similarly to the way you use an immutable one, but instead
of enqueue, you use the += and ++= operators to append. Also, on a mutable queue, the dequeue method will just remove the head element from the
queue and return it. Here’s an example:
scala> val queue = new scala.collection.mutable.Queue[String]
queue: scala.collection.mutable.Queue[String] = Queue()
scala> queue += "a"
res47: queue.type = Queue(a)
scala> queue ++= List("b", "c")
res48: queue.type = Queue(a, b, c)
scala> queue
res49: scala.collection.mutable.Queue[String] = Queue(a, b, c)
scala> queue.dequeue
res50: String = a
scala> queue
res51: scala.collection.mutable.Queue[String] = Queue(b, c)
Array sequences
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Array sequences are mutable sequences of fixed size that store their elements
internally in an Array[AnyRef]. They are implemented in Scala by class
ArraySeq.
You would typically use an ArraySeq if you want an array for its performance characteristics, but you also want to create generic instances of the
sequence where you do not know the type of the elements and do not have a
ClassTag to provide it at run-time. You will find out about these issues with
arrays shortly, in Section 24.10.
Stacks
You saw immutable stacks earlier. There is also a mutable version. It works
exactly the same as the immutable version except that modifications happen
in place. Here’s an example:
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scala> val stack = new scala.collection.mutable.Stack[Int]
stack: scala.collection.mutable.Stack[Int] = Stack()
scala> stack.push(1)
res52: stack.type = Stack(1)
scala> stack
res53: scala.collection.mutable.Stack[Int] = Stack(1)
scala> stack.push(2)
res54: stack.type = Stack(2, 1)
scala> stack
res55: scala.collection.mutable.Stack[Int] = Stack(2, 1)
scala> stack.top
res56: Int = 2
scala> stack
res57: scala.collection.mutable.Stack[Int] = Stack(2, 1)
scala> stack.pop
res58: Int = 2
scala> stack
res59: scala.collection.mutable.Stack[Int] = Stack(1)
Array stacks
ArrayStack is an alternative implementation of a mutable stack, which is
backed by an Array that gets resized as needed. It provides fast indexing
and is generally slightly more efficient for most operations than a normal
mutable stack.
Prepared for Library Genesis
Hash tables
A hash table stores its elements in an underlying array, placing each item at
a position in the array determined by the hash code of that item. Adding an
element to a hash table takes only constant time, so long as there isn’t already
another element in the array that has the same hash code. Hash tables are thus
very fast so long as the objects placed in them have a good distribution of
hash codes. As a result, the default mutable map and set types in Scala are
based on hash tables.
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Hash sets and maps are used just like any other set or map. Here are
some simple examples:
scala> val map = collection.mutable.HashMap.empty[Int,String]
map: scala.collection.mutable.HashMap[Int,String] = Map()
scala> map += (1 -> "make a web site")
res60: map.type = Map(1 -> make a web site)
scala> map += (3 -> "profit!")
res61: map.type = Map(1 -> make a web site, 3 -> profit!)
scala> map(1)
res62: String = make a web site
scala> map contains 2
res63: Boolean = false
Iteration over a hash table is not guaranteed to occur in any particular
order. Iteration simply proceeds through the underlying array in whichever
order it happens to be. To get a guaranteed iteration order, use a linked hash
map or set instead of a regular one. A linked hash map or set is just like
a regular hash map or set except that it also includes a linked list of the
elements in the order they were added. Iteration over such a collection is
always in the same order that the elements were initially added.
Prepared for Library Genesis
Weak hash maps
A weak hash map is a special kind of hash map in which the garbage collector
does not follow links from the map to the keys stored in it. This means that a
key and its associated value will disappear from the map if there is no other
reference to that key. Weak hash maps are useful for tasks such as caching,
where you want to re-use an expensive function’s result if the function is
called again on the same key. If keys and function results are stored in a
regular hash map, the map could grow without bounds, and no key would
ever become garbage. Using a weak hash map avoids this problem. As soon
as a key object becomes unreachable, it’s entry is removed from the weak
hash map. Weak hash maps in Scala are implemented as a wrapper of an
underlying Java implementation, java.util.WeakHashMap.
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Concurrent Maps
A concurrent map can be accessed by several threads at once. In addition to
the usual Map operations, it provides the following atomic operations:
Table 24.9 · Operations in trait ConcurrentMap
What it is
What it does
m putIfAbsent(k, v)
Adds key/value binding k -> m unless k is already
defined in m
m remove (k, v)
Removes entry for k if it is currently mapped to v
m replace (k, old, new)
Replaces value associated with key k to new, if it
was previously bound to old
m replace (k, v)
Replaces value associated with key k to v, if it
was previously bound to some value
ConcurrentMap is a trait in the Scala collections library. Currently, its
only implementation is Java’s java.util.concurrent.ConcurrentMap,
which can be converted automatically into a Scala map using the standard
Java/Scala collection conversions, which will be described in Section 24.17.
Mutable bit sets
A mutable bit set is just like an immutable one, except that it can be modified in place. Mutable bit sets are slightly more efficient at updating than
immutable ones, because they don’t have to copy around Longs that haven’t
changed. Here is an example:
scala> val bits = scala.collection.mutable.BitSet.empty
bits: scala.collection.mutable.BitSet = BitSet()
scala> bits += 1
Prepared for Library Genesis
res64: bits.type = BitSet(1)
scala> bits += 3
res65: bits.type = BitSet(1, 3)
scala> bits
res66: scala.collection.mutable.BitSet = BitSet(1, 3)
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Chapter 24 · Collections in Depth
Arrays
Arrays are a special kind of collection in Scala. One the one hand, Scala arrays correspond one-to-one to Java arrays. That is, a Scala array Array[Int]
is represented as a Java int[], an Array[Double] is represented as a Java
double[] and an Array[String] is represented as a Java String[]. But
at the same time, Scala arrays offer much more their Java analogues. First,
Scala arrays can be generic. That is, you can have an Array[T], where T is
a type parameter or abstract type. Second, Scala arrays are compatible with
Scala sequences—you can pass an Array[T] where a Seq[T] is required.
Finally, Scala arrays also support all sequence operations. Here’s an example of this in action:
scala> val a1 = Array(1, 2, 3)
a1: Array[Int] = Array(1, 2, 3)
scala> val a2 = a1 map (_ * 3)
a2: Array[Int] = Array(3, 6, 9)
scala> val a3 = a2 filter (_ % 2 != 0)
a3: Array[Int] = Array(3, 9)
scala> a3.reverse
res1: Array[Int] = Array(9, 3)
Given that Scala arrays are represented just like Java arrays, how can these
additional features be supported in Scala?
The answer lies in systematic use of implicit conversions. An array cannot pretend to be a sequence, because the data type representation of a native
array is not a subtype of Seq. Instead, whenever an array would be used as
a Seq, implicitly wrap it in a subclass of Seq. The name of that subclass is
scala.collection.mutable.WrappedArray. Here you see it in action:
scala> val seq: Seq[Int] = a1
Prepared for Library Genesis
seq: Seq[Int] = WrappedArray(1, 2, 3)
scala> val a4: Array[Int] = seq.toArray
a4: Array[Int] = Array(1, 2, 3)
scala> a1 eq a4
res2: Boolean = true
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This interaction demonstrates that arrays are compatible with sequences, because there’s an implicit conversion from Array to WrappedArray. To go
the other way, from a WrappedArray to an Array, you can use the toArray
method defined in Traversable. The last interpreter line above shows that
wrapping then unwrapping with toArray gives you back the same array you
started with.
There is yet another implicit conversion that gets applied to arrays. This
conversion simply “adds” all sequence methods to arrays but does not turn
the array itself into a sequence. “Adding” means that the array is wrapped
in another object of type ArrayOps, which supports all sequence methods.
Typically, this ArrayOps object is short-lived; it will usually be inaccessible
after the call to the sequence method and its storage can be recycled. Modern
VMs often avoid creating this object entirely.
The difference between the two implicit conversions on arrays is demonstrated here:
scala> val seq: Seq[Int] = a1
seq: Seq[Int] = WrappedArray(1, 2, 3)
scala> seq.reverse
res2: Seq[Int] = WrappedArray(3, 2, 1)
scala> val ops: collection.mutable.ArrayOps[Int] = a1
ops: scala.collection.mutable.ArrayOps[Int] = [I(1, 2, 3)
scala> ops.reverse
Prepared for Library Genesis
res3: Array[Int] = Array(3, 2, 1)
You see that calling reverse on seq, which is a WrappedArray, will give
again a WrappedArray. That’s logical, because wrapped arrays are Seqs,
and calling reverse on any Seq will give again a Seq. On the other hand,
calling reverse on the ops value of class ArrayOps will result in an Array,
not a Seq.
The ArrayOps example above was quite artificial, intended only to show
the difference to WrappedArray. Normally, you’d never define a value of
class ArrayOps. You’d just call a Seq method on an array:
scala> a1.reverse
res4: Array[Int] = Array(3, 2, 1)
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The ArrayOps object gets inserted automatically by the implicit conversion.
So the line above is equivalent to the following line, where intArrayOps
was the conversion that was implicitly inserted previously:
scala> intArrayOps(a1).reverse
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res5: Array[Int] = Array(3, 2, 1)
This raises the question how the compiler picked intArrayOps over the
other implicit conversion to WrappedArray in the line above. After all, both
conversions map an array to a type that supports a reverse method, which is
what the input specified. The answer to that question is that the two implicit
conversions are prioritized. The ArrayOps conversion has a higher priority
than the WrappedArray conversion. The first is defined in the Predef object
whereas the second is defined in a class scala.LowPriorityImplicits,
which is a superclass of Predef. Implicits in subclasses and subobjects take
precedence over implicits in base classes. So if both conversions are applicable, the one in Predef is chosen. A very similar scheme, which was
described in Section 21.7, works for strings.
So now you know how arrays can be compatible with sequences and how
they can support all sequence operations. What about genericity? In Java
you cannot write a T[] where T is a type parameter. How then is Scala’s
Array[T] represented? In fact a generic array like Array[T] could be at run
time any of Java’s eight primitive array types byte[], short[], char[],
int[], long[], float[], double[], boolean[], or it could be an array of
objects. The only common run-time type encompassing all of these types
is AnyRef (or, equivalently java.lang.Object), so that’s the type to which
the Scala compiler maps Array[T]. At run-time, when an element of an array of type Array[T] is accessed or updated there is a sequence of type tests
that determine the actual array type, followed by the correct array operation
on the Java array. These type tests slow down array operations somewhat.
You can expect accesses to generic arrays to be three to four times slower
than accesses to primitive or object arrays. This means that if you need maximal performance, you should prefer concrete over generic arrays.
Representing the generic array type is not enough, however, there must
also be a way to create generic arrays. This is an even harder problem, which
requires a little bit of help from you. To illustrate the problem, consider the
following attempt to write a generic method that creates an array:
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// This is wrong!
def evenElems[T](xs: Vector[T]): Array[T] = {
val arr = new Array[T]((xs.length + 1) / 2)
for (i <- 0 until xs.length by 2)
arr(i / 2) = xs(i)
arr
}
The evenElems method returns a new array that consists of all elements of
the argument vector xs that are at even positions in the vector. The first
line of the body of evenElems creates the result array, which has the same
element type as the argument. So depending on the actual type parameter for
T, this could be an Array[Int], or an Array[Boolean], or an array of some
of the other primitive types in Java, or an array of some reference type. But
these types all have different runtime representations, so how is the Scala
runtime going to pick the correct one? In fact, it can’t do that based on the
information it is given, because the actual type that corresponds to the type
parameter T is erased at runtime. That’s why you will get the following error
message if you attempt to compile the code above:
error: cannot find class tag for element type T
val arr = new Array[T]((arr.length + 1) / 2)
Prepared for Library Genesis
ˆ
What’s required here is that you help the compiler by providing a runtime
hint of what the actual type parameter of evenElems is. This runtime hint
takes the form of a class tag of type scala.reflect.ClassTag. A class tag
describes the erased type of a given type, which is all the information needed
to construct an array of that type.
In many cases the compiler can generate a class tag on its own. Such is
the case for a concrete type like Int or String. It’s also the case for certain
generic types, like List[T], where enough information is known to predict
the erased type; in this example the erased type would be List.
For fully generic cases, the usual idiom is to pass the class tag using a
context bound, as discussed in Section 21.6. Here is how the above definition
could be fixed by using a context bound:
// This works
import scala.reflect.ClassTag
def evenElems[T: ClassTag](xs: Vector[T]): Array[T] = {
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val arr = new Array[T]((xs.length + 1) / 2)
for (i <- 0 until xs.length by 2)
arr(i / 2) = xs(i)
arr
}
In this new definition, when the Array[T] is created, the compiler looks for
a class tag for the type parameter T, that is, it will look for an implicit value of
type ClassTag[T]. If such a value is found, the class tag is used to construct
the right kind of array. Otherwise, you’ll see an error message like the one
shown previously.
Here is an interpreter interaction that uses the evenElems method:
scala> evenElems(Vector(1, 2, 3, 4, 5))
res6: Array[Int] = Array(1, 3, 5)
scala> evenElems(Vector("this", "is", "a", "test", "run"))
res7: Array[java.lang.String] = Array(this, a, run)
In both cases, the Scala compiler automatically constructed a class tag for the
element type (first Int, then String) and passed it to the implicit parameter
of the evenElems method. The compiler can do that for all concrete types,
but not if the argument is itself another type parameter without its class tag.
For instance, the following fails:
scala> def wrap[U](xs: Vector[U]) = evenElems(xs)
:9: error: No ClassTag available for U
def wrap[U](xs: Vector[U]) = evenElems(xs)
ˆ
What happened here is that the evenElems demands a class tag for the type
parameter U, but none was found. The solution in this case is, of course, to
demand another implicit class tag for U. So the following works:
Prepared for Library Genesis
scala> def wrap[U: ClassTag](xs: Vector[U]) = evenElems(xs)
wrap: [U](xs: Vector[U])(implicit evidence$1:
scala.reflect.ClassTag[U])Array[U]
This example also shows that the context bound in the definition of U is
just a shorthand for an implicit parameter named here evidence$1 of type
ClassTag[U].
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Chapter 24 · Collections in Depth
Strings
Like arrays, strings are not directly sequences, but they can be converted to
them, and they also support all sequence operations. Here are some examples
of operations you can invoke on strings:
scala> val str = "hello"
str: java.lang.String = hello
scala> str.reverse
res6: String = olleh
scala> str.map(_.toUpper)
res7: String = HELLO
scala> str drop 3
res8: String = lo
scala> str slice (1, 4)
res9: String = ell
scala> val s: Seq[Char] = str
s: Seq[Char] = WrappedString(h, e, l, l, o)
These operations are supported by two implicit conversions, which were explained in Section 21.7. The first, low-priority conversion maps a String
to a WrappedString, which is a subclass of immutable.IndexedSeq. This
conversion was applied in the last line of the previous example in which a
string was converted into a Seq. The other, high-priority conversion maps
a string to a StringOps object, which adds all methods on immutable sequences to strings. This conversion was implicitly inserted in the method
calls of reverse, map, drop, and slice in the previous example.
Prepared for Library Genesis
24.12
Performance characteristics
As the previous explanations have shown, different collection types have
different performance characteristics. That’s often the primary reason for
picking one collection type over another. You can see the performance characteristics of some common operations on collections summarized in two
tables, Table 24.10 and Table 24.11.
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576
head
tail
apply
update
prepend
append
insert
C
C
eC
C
aC
C
C
C
C
eC
C
aC
C
L
L
L
eC
L
L
C
C
L
L
eC
L
L
L
C
C
eC
C
L
L
L
L
eC
L
C
L
-
C
C
C
C
C
C
C
C
C
L
L
L
L
L
L
L
L
L
C
L
C
L
L
C
L
C
C
C
L
C
L
L
C
L
C
C
L
C
L
C
C
C
aC
-
aC
C
aC
C
C
L
L
-
L
L
L
L
L
L
L
-
immutable
List
Stream
Vector
Stack
Queue
Range
String
mutable
ArrayBuffer
ListBuffer
StringBuilder
MutableList
Queue
ArraySeq
Stack
ArrayStack
Array
Table 24.10 · Performance characteristics of sequence types
lookup
add
remove
min
eC
Log
C
L
eC
Log
L
L
eC
Log
L
L
L
Log
eCa
L
eC
eC
C
eC
eC
aC
eC
eC
C
L
L
eCa
immutable
HashSet/HashMap
TreeSet/TreeMap
BitSet
ListMap
Prepared for Library Genesis
mutable
HashSet/HashMap
WeakHashMap
BitSet
Table 24.11 · Performance characteristics of set and map types
a Assuming
bits are densely packed.
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Section 24.12
Chapter 24 · Collections in Depth
The entries in these two tables are explained as follows:
C
eC
aC
Log
L
-
The operation takes (fast) constant time.
The operation takes effectively constant time, but
this might depend on some assumptions such as the
maximum length of a vector or the distribution of
hash keys.
The operation takes amortized constant time. Some
invocations of the operation might take longer, but
if many operations are performed on average only
constant time per operation is taken.
The operation takes time proportional to the logarithm of the collection size.
The operation is linear, that is it takes time proportional to the collection size.
The operation is not supported.
Table 24.10 treats sequence types—both immutable and mutable—with
the following operations:
head
tail
apply
update
prepend
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append
insert
Selecting the first element of the sequence.
Producing a new sequence that consists of all elements except the first one.
Indexing.
Functional update (with updated) for immutable
sequences, side-effecting update (with update) for
mutable sequences.
Adding an element to the front of the sequence.
For immutable sequences, this produces a new sequence. For mutable sequences it modifies the existing sequence.
Adding an element at the end of the sequence.
For immutable sequences, this produces a new sequence. For mutable sequences it modifies the existing sequence.
Inserting an element at an arbitrary position in the
sequence. This is only supported directly for mutable sequences.
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Table 24.11 treats mutable and immutable sets and maps with the following operations:
lookup
add
remove
min
24.13
Testing whether an element is contained in set, or
selecting a value associated with a key.
Adding a new element to a set or a new key/value
pair to a map.
Removing an element from a set or a key from a
map.
The smallest element of the set, or the smallest key
of a map.
Equality
The collection libraries have a uniform approach to equality and hashing.
The idea is, first, to divide collections into sets, maps, and sequences. Collections in different categories are always unequal. For instance, Set(1, 2, 3)
is unequal to List(1, 2, 3) even though they contain the same elements.
On the other hand, within the same category, collections are equal if and
only if they have the same elements (for sequences: the same elements in
the same order). For example, List(1, 2, 3) == Vector(1, 2, 3), and
HashSet(1, 2) == TreeSet(2, 1).
It does not matter for the equality check whether a collection is mutable
or immutable. For a mutable collection, equality simply depends on the
current elements at the time the equality test is performed. This means that a
mutable collection might be equal to different collections at different times,
depending what elements are added or removed. This is a potential trap when
using a mutable collection as a key in a hash map. For example:
scala> import collection.mutable.{HashMap, ArrayBuffer}
import collection.mutable.{HashMap, ArrayBuffer}
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scala> val buf = ArrayBuffer(1, 2, 3)
buf: scala.collection.mutable.ArrayBuffer[Int] =
ArrayBuffer(1, 2, 3)
scala> val map = HashMap(buf -> 3)
map: scala.collection.mutable.HashMap[scala.collection.
mutable.ArrayBuffer[Int],Int] = Map((ArrayBuffer(1, 2, 3),3))
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scala> map(buf)
res13: Int = 3
scala> buf(0) += 1
scala> map(buf)
java.util.NoSuchElementException: key not found:
ArrayBuffer(2, 2, 3)
In this example, the selection in the last line will most likely fail because the
hash code of the array xs has changed in the second-to-last line. Therefore,
the hash-code-based lookup will look at a different place than the one in
which xs was stored.
24.14
Views
Collections have quite a few methods that construct new collections. Some
examples are map, filter, and ++. We call such methods transformers because they take at least one collection as their receiver object and produce
another collection in their result.
Transformers can be implemented in two principal ways: strict and nonstrict (or lazy). A strict transformer constructs a new collection with all of its
elements. A non-strict, or lazy, transformer constructs only a proxy for the
result collection, and its elements are constructed on demand.
As an example of a non-strict transformer, consider the following implementation of a lazy map operation:
def lazyMap[T, U](coll: Iterable[T], f: T => U) =
new Iterable[U] {
def iterator = coll.iterator map f
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}
Note that lazyMap constructs a new Iterable without stepping through all
elements of the given collection coll. The given function f is instead applied to the elements of the new collection’s iterator as they are demanded.
Scala collections are by default strict in all their transformers, except
for Stream, which implements all its transformer methods lazily. However,
there is a systematic way to turn every collection into a lazy one and vice
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versa, which is based on collection views. A view is a special kind of collection that represents some base collection, but implements all of its transformers lazily.
To go from a collection to its view, you can use the view method on the
collection. If xs is some collection, then xs.view is the same collection, but
with all transformers implemented lazily. To get back from a view to a strict
collection, you can use the force method.
As an example, say you have a vector of Ints over which you want to
map two functions in succession:
scala> val v = Vector(1 to 10: _*)
v: scala.collection.immutable.Vector[Int] =
Vector(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
scala> v map (_ + 1) map (_ * 2)
res5: scala.collection.immutable.Vector[Int] =
Vector(4, 6, 8, 10, 12, 14, 16, 18, 20, 22)
In the last statement, the expression v map (_ + 1) constructs a new vector
that is then transformed into a third vector by the second call to map (_ * 2).
In many situations, constructing the intermediate result from the first call
to map is a bit wasteful. In the pseudo example, it would be faster to do a
single map with the composition of the two functions (_ + 1) and (_ * 2).
If you have the two functions available in the same place you can do this
by hand. But quite often, successive transformations of a data structure are
done in different program modules. Fusing those transformations would then
undermine modularity. A more general way to avoid the intermediate results
is by turning the vector first into a view, applying all transformations to the
view, and finally forcing the view to a vector:
scala> (v.view map (_ + 1) map (_ * 2)).force
res12: Seq[Int] = Vector(4, 6, 8, 10, 12, 14, 16, 18, 20, 22)
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We’ll do this sequence of operations again, one by one:
scala> val vv = v.view
vv: scala.collection.SeqView[Int,Vector[Int]] =
SeqView(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
The application v.view gives you a SeqView, i.e., a lazily evaluated Seq.
The type SeqView has two type parameters. The first, Int, shows the type
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of the view’s elements. The second, Vector[Int], shows you the type constructor you get back when forcing the view.
Applying the first map to the view gives you:
scala> vv map (_ + 1)
res13: scala.collection.SeqView[Int,Seq[_]] = SeqViewM(...)
The result of the map is a value that prints SeqViewM(...). This is in essence
a wrapper that records the fact that a map with function (_ + 1) needs to be
applied on the vector v. It does not apply that map until the view is forced,
however. The “M” after SeqView is an indication that the view encapsulates a
map operation. Other letters indicate other delayed operations. For instance
“S” indicates a delayed slice operation, and “R” indicates a reverse. We’ll
now apply the second map to the last result.
scala> res13 map (_ * 2)
res14: scala.collection.SeqView[Int,Seq[_]] = SeqViewMM(...)
You now get a SeqView that contains two map operations, so it prints with a
double “M”: SeqViewMM(...). Finally, forcing the last result gives:
scala> res14.force
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res15: Seq[Int] = Vector(4, 6, 8, 10, 12, 14, 16, 18, 20, 22)
Both stored functions get applied as part of the execution of the force operation and a new vector is constructed. That way, no intermediate data structure
is needed.
One detail to note is that the static type of the final result is a Seq, not a
Vector. Tracing the types back we see that as soon as the first delayed map
was applied, the result had static type SeqViewM[Int, Seq[_]]. That is, the
“knowledge” that the view was applied to the specific sequence type Vector
got lost. The implementation of a view for any particular class requires quite
a bit of code, so the Scala collection libraries provide views mostly only for
general collection types, not for specific implementations.5
There are two reasons why you might want to consider using views. The
first is performance. You have seen that by switching a collection to a view
the construction of intermediate results can be avoided. These savings can
5 An exception to this is arrays: applying delayed operations on arrays will again give
results with static type Array.
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be quite important. As another example, consider the problem of finding
the first palindrome in a list of words. A palindrome is a word that reads
backwards the same as forwards. Here are the necessary definitions:
def isPalindrome(x: String) = x == x.reverse
def findPalindrome(s: Seq[String]) = s find isPalindrome
Now, assume you have a very long sequence words and you want to find a
palindrome in the first million words of that sequence. Can you re-use the
definition of findPalindrome? Of course, you could write:
findPalindrome(words take 1000000)
This nicely separates the two aspects of taking the first million words of
a sequence and finding a palindrome in it. But the downside is that it always constructs an intermediary sequence consisting of one million words,
even if the first word of that sequence is already a palindrome. So potentially, 999,999 words are copied into the intermediary result without being
inspected at all afterwards. Many programmers would give up here and write
their own specialized version of finding palindromes in some given prefix of
an argument sequence. But with views, you don’t have to. Simply write:
findPalindrome(words.view take 1000000)
This has the same nice separation of concerns, but instead of a sequence of a
million elements it will only construct a single lightweight view object. This
way, you do not need to choose between performance and modularity.
The second use case applies to views over mutable sequences. Many
transformer functions on such views provide a window into the original sequence that can then be used to update selectively some elements of that
sequence. To see this in an example, suppose you have an array arr:
scala> val arr = (0 to 9).toArray
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arr: Array[Int] = Array(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
You can create a subwindow into that array by creating a slice of a view of
the array:
scala> val subarr = arr.view.slice(3, 6)
subarr: scala.collection.mutable.IndexedSeqView[
Int,Array[Int]] = IndexedSeqViewS(...)
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This gives a view, subarr, which refers to the elements at positions 3 through
5 of the array arr. The view does not copy these elements, it just provides
a reference to them. Now, assume you have a method that modifies some
elements of a sequence. For instance, the following negate method would
negate all elements of the sequence of integers it’s given:
scala> def negate(xs: collection.mutable.Seq[Int]) =
for (i <- 0 until xs.length) xs(i) = -xs(i)
negate: (xs: scala.collection.mutable.Seq[Int])Unit
Assume now you want to negate elements at positions three through five of
the array arr. Can you use negate for this? Using a view, this is simple:
scala> negate(subarr)
scala> arr
res4: Array[Int] = Array(0, 1, 2, -3, -4, -5, 6, 7, 8, 9)
What happened here is that negate changed all elements of subarr, which
were a slice of the elements of arr. Again, you see that views help in keeping
things modular. The code above nicely separated the question of what index
range to apply a method to from the question what method to apply.
After having seen all these nifty uses of views you might wonder why
have strict collections at all? One reason is that performance comparisons do
not always favor lazy over strict collections. For smaller collection sizes the
added overhead of forming and applying closures in views is often greater
than the gain from avoiding the intermediary data structures. A possibly
more important reason is that evaluation in views can be very confusing if
the delayed operations have side effects.
Here’s an example that bit a few users of versions of Scala before 2.8. In
these versions the Range type was lazy, so it behaved in effect like a view.
People were trying to create a number of actors6 like this:
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val actors = for (i <- 1 to 10) yield actor { ... }
They were surprised that none of the actors were executing afterwards, even
though the actor method should create and start an actor from the code
that’s enclosed in the braces following it. To explain why nothing happened,
remember that the for expression above is equivalent to an application of
the map method:
6 The Scala actors library has been deprecated, but this historical example is still relevant.
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val actors = (1 to 10) map (i => actor { ... })
Since previously the range produced by (1 to 10) behaved like a view, the
result of the map was again a view. That is, no element was computed, and,
consequently, no actor was created! Actors would have been created by
forcing the range of the whole expression, but it’s far from obvious that this
is what was required to make the actors do their work.
To avoid surprises like this, the Scala collections gained more regular
rules in version 2.8. All collections except streams and views are strict. The
only way to go from a strict to a lazy collection is via the view method. The
only way to go back is via force. So the actors definition above would
behave as expected in Scala 2.8 in that it would create and start ten actors.
To get back the surprising previous behavior, you’d have to add an explicit
view method call:
val actors = for (i <- (1 to 10).view) yield actor { ... }
In summary, views are a powerful tool to reconcile concerns of efficiency
with concerns of modularity. But in order not to be entangled in aspects of
delayed evaluation, you should restrict views to two scenarios. Either you
apply views in purely functional code where collection transformations do
not have side effects. Or you apply them over mutable collections where all
modifications are done explicitly. What’s best avoided is a mixture of views
and operations that create new collections while also having side effects.
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24.15
Iterators
An iterator is not a collection, but rather a way to access the elements of
a collection one by one. The two basic operations on an iterator it are
next and hasNext. A call to it.next() will return the next element of
the iterator and advance the state of the iterator. Calling next again on the
same iterator will then yield the element one beyond the one returned previously. If there are no more elements to return, a call to next will throw a
NoSuchElementException. You can find out whether there are more elements to return using Iterator’s hasNext method.
The most straightforward way to “step through” all the elements returned
by an iterator is to use a while loop:
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while (it.hasNext)
println(it.next())
Iterators in Scala also provide analogues of most of the methods that you find
in the Traversable, Iterable, and Seq traits. For instance, they provide a
foreach method that executes a given procedure on each element returned
by an iterator. Using foreach, the loop above could be abbreviated to:
it foreach println
As always, for expressions can be used as an alternate syntax for expressions
involving foreach, map, filter, and flatMap, so yet another way to print
all elements returned by an iterator would be:
for (elem <- it) println(elem)
There’s an important difference between the foreach method on iterators
and the same method on traversable collections: When called on an iterator, foreach will leave the iterator at its end when it is done. So calling
next again on the same iterator will fail with a NoSuchElementException.
By contrast, when called on a collection, foreach leaves the number of elements in the collection unchanged (unless the passed function adds or removes elements, but this is discouraged, because it can easily lead to surprising results).
The other operations that Iterator has in common with Traversable
have the same property of leaving the iterator at its end when done. For
instance, iterators provide a map method, which returns a new iterator:
scala> val it = Iterator("a", "number", "of", "words")
it: Iterator[java.lang.String] = non-empty iterator
scala> it.map(_.length)
res1: Iterator[Int] = non-empty iterator
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scala> res1 foreach println
1
6
2
5
scala> it.next()
java.util.NoSuchElementException: next on empty iterator
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As you can see, after the call to map, the it iterator has advanced to its end.
Another example is the dropWhile method, which can be used to find
the first element of an iterator that has a certain property. For instance, to find
the first word in the iterator shown previously that has at least two characters,
you could write:
scala> val it = Iterator("a", "number", "of", "words")
it: Iterator[java.lang.String] = non-empty iterator
scala> it dropWhile (_.length < 2)
res4: Iterator[java.lang.String] = non-empty iterator
scala> it.next()
res5: java.lang.String = number
Note again that it has changed by the call to dropWhile: it now points to
the second word “number” in the list. In fact, it and the result res4 returned
by dropWhile will return exactly the same sequence of elements.
There is only one standard operation, duplicate, which allows you to
re-use the same iterator:
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val (it1, it2) = it.duplicate
The call to duplicate gives you two iterators, which each return exactly the
same elements as the iterator it. The two iterators work independently;
advancing one does not affect the other. By contrast the original iterator, it,
is advanced to its end by duplicate and is thus rendered unusable.
In summary, iterators behave like collections if you never access an iterator again after invoking a method on it. The Scala collection libraries
make this explicit with an abstraction called TraversableOnce, which is a
common supertrait of Traversable and Iterator. As the name implies,
TraversableOnce objects can be traversed using foreach, but the state of
that object after the traversal is not specified. If the TraversableOnce object is in fact an Iterator, it will be at its end after the traversal, but if
it is a Traversable, it will still exist as before. A common use case of
TraversableOnce is as an argument type for methods that can take either
an iterator or traversable as argument. An example is the appending method
++ in trait Traversable. It takes a TraversableOnce parameter, so you can
append elements coming from either an iterator or a traversable collection.
All operations on iterators are summarized in Table 24.12.
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Table 24.12 · Operations in trait Iterator
What it is
What it does
Abstract methods:
it.next()
Returns the next element in the iterator and
advances past it.
it.hasNext
Returns true if it can return another element.
Variations:
it.buffered
A buffered iterator returning all elements of it.
it grouped size
An iterator that yields the elements returned by
it in fixed-sized sequence “chunks.”
xs sliding size
An iterator that yields the elements returned by
it in sequences representing a sliding fixed-sized
window.
Copying:
it copyToBuffer buf
Copies all elements returned by it to buffer buf.
it copyToArray(arr, s, l)
Copies at most l elements returned by it to array
arr starting at index s. The last two arguments
are optional.
Duplication:
it.duplicate
A pair of iterators that each independently return
all elements of it.
Additions:
it ++ jt
An iterator returning all elements returned by
iterator it, followed by all elements returned by
iterator jt.
it padTo (len, x)
The iterator that returns all elements of it
followed by copies of x until length len elements
are returned overall.
it map f
The iterator obtained from applying the function
f to every element returned from it.
it flatMap f
The iterator obtained from applying the
iterator-valued function f to every element in it
and appending the results.
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Maps:
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Table 24.12 · continued
it collect f
The iterator obtained from applying the partial
function f to every element in it for which it is
defined and collecting the results.
Conversions:
it.toArray
it.toList
Collects the elements returned by it in an array.
Collects the elements returned by it in a list.
it.toIterable
Collects the elements returned by it in an
iterable.
it.toSeq
Collects the elements returned by it in a
sequence.
it.toIndexedSeq
Collects the elements returned by it in an
indexed sequence.
it.toStream
Collects the elements returned by it in a stream.
it.toSet
Collects the elements returned by it in a set.
it.toMap
Collects the key/value pairs returned by it in a
map.
it.isEmpty
Tests whether the iterator is empty (opposite of
hasNext).
it.nonEmpty
Tests whether the collection contains elements
(alias of hasNext).
Size info:
it.size
it.length
it.hasDefiniteSize
The number of elements returned by it. Note: it
will be at its end after this operation!
Same as it.size.
Returns true if it is known to return finitely
many elements (by default the same as isEmpty).
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Element retrieval index search:
it find p
An option containing the first element returned by
it that satisfies p, or None if no element qualifies.
Note: The iterator advances to just after the
element, or, if none is found, to the end.
it indexOf x
The index of the first element returned by it that
equals x. Note: The iterator advances past the
position of this element.
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Table 24.12 · continued
it indexWhere p
The index of the first element returned by it that
satisfies p. Note: The iterator advances past the
position of this element.
Subiterators:
it take n
An iterator returning of the first n elements of it.
Note: it will advance to the position after the
n’th element, or to its end, if it contains less than
n elements.
it drop n
The iterator that starts with the (n + 1)’th element
of it. Note: it will advance to the same position.
it slice (m, n)
The iterator that returns a slice of the elements
returned from it, starting with the m’th element
and ending before the n’th element.
it takeWhile p
An iterator returning elements from it as long as
condition p is true.
it dropWhile p
An iterator skipping elements from it as long as
condition p is true, and returning the remainder.
it filter p
An iterator returning all elements from it that
satisfy the condition p.
it withFilter p
Same as it filter p. Needed so that iterators
can be used in for expressions.
it filterNot p
An iterator returning all elements from it that do
not satisfy the condition p.
Subdivisions:
it partition p
Splits it into a pair of two iterators; one
returning all elements from it that satisfy the
predicate p, the other returning all elements from
it that do not.
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Element conditions:
it forall p
A boolean indicating whether the predicate p
holds for all elements returned by it.
it exists p
A boolean indicating whether the predicate p
holds for some element in it.
it count p
The number of elements in it that satisfy the
predicate p.
Folds:
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Table 24.12 · continued
(z /: it)(op)
Applies binary operation op between successive
elements returned by it, going left to right,
starting with z.
(it :\ z)(op)
Applies binary operation op between successive
elements returned by it, going right to left,
starting with z.
it.foldLeft(z)(op)
Same as (z /: it)(op).
it.foldRight(z)(op)
Same as (it :\ z)(op).
it reduceLeft op
Applies binary operation op between successive
elements returned by non-empty iterator it,
going left to right.
it reduceRight op
Applies binary operation op between successive
elements returned by non-empty iterator it,
going right to left.
Specific folds:
it.sum
it.product
The sum of the numeric element values returned
by iterator it.
The product of the numeric element values
returned by iterator it.
it.min
The minimum of the ordered element values
returned by iterator it.
it.max
The maximum of the ordered element values
returned by iterator it.
Zippers:
it zip jt
An iterator of pairs of corresponding elements
returned from iterators it and jt.
it zipAll (jt, x, y)
An iterator of pairs of corresponding elements
returned from iterators it and jt, where the
shorter iterator is extended to match the longer
one by appending elements x or y.
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it.zipWithIndex
An iterator of pairs of elements returned from it
with their indicies.
Update:
it patch (i, jt, r)
The iterator resulting from it by replacing r
elements starting with i by the patch iterator jt.
Comparison:
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Table 24.12 · continued
it sameElements jt
A test whether iterators it and jt return the same
elements in the same order. Note: At least one of
it and jt will be at its end after this operation.
Strings:
it addString (b, start,
sep, end)
Adds a string to StringBuilder b that shows all
elements returned by it between separators sep
enclosed in strings start and end. start,sep,
and end are all optional.
it mkString (start,
sep, end)
Converts the iterator to a string that shows all
elements returned by it between separators sep
enclosed in strings start and end. start,sep,
and end are all optional.
Buffered iterators
Sometimes you want an iterator that can “look ahead” so that you can inspect the next element to be returned without advancing past that element.
Consider, for instance, the task to skip leading empty strings from an iterator
that returns a sequence of strings. You might be tempted to write something
like the following method:
// This won't work
def skipEmptyWordsNOT(it: Iterator[String]) = {
while (it.next().isEmpty) {}
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}
But looking at this code more closely, it’s clear that this is wrong: the code
will indeed skip leading empty strings, but it will also advance it past the
first non-empty string!
The solution to this problem is to use a buffered iterator, an instance of
trait BufferedIterator. BufferedIterator is a subtrait of Iterator,
which provides one extra method, head. Calling head on a buffered iterator
will return its first element, but will not advance the iterator. Using a buffered
iterator, skipping empty words can be written like this:
def skipEmptyWords(it: BufferedIterator[String]) =
while (it.head.isEmpty) { it.next() }
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Every iterator can be converted to a buffered iterator by calling its buffered
method. Here’s an example:
scala> val it = Iterator(1, 2, 3, 4)
it: Iterator[Int] = non-empty iterator
scala> val bit = it.buffered
bit: java.lang.Object with scala.collection.
BufferedIterator[Int] = non-empty iterator
scala> bit.head
res10: Int = 1
scala> bit.next()
res11: Int = 1
scala> bit.next()
res11: Int = 2
Note that calling head on the buffered iterator, bit, did not advance it.
Therefore, the subsequent call, bit.next(), returned again the same value
as bit.head.
24.16
Creating collections from scratch
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You have already seen syntax like List(1, 2, 3), which creates a list of
three integers, and Map('A' -> 1, 'C' -> 2), which creates a map with two
bindings. This is actually a universal feature of Scala collections. You can
take any collection name and follow it by a list of elements in parentheses.
The result will be a new collection with the given elements. Here are some
more examples:
Traversable()
// An empty traversable object
List()
// The empty list
List(1.0, 2.0)
// A list with elements 1.0, 2.0
Vector(1.0, 2.0)
// A vector with elements 1.0, 2.0
Iterator(1, 2, 3)
// An iterator returning three integers.
Set(dog, cat, bird)
// A set of three animals
HashSet(dog, cat, bird)
// A hash set of the same animals
Map('a' -> 7, 'b' -> 0)
// A map from characters to integers
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“Under the covers” each of the above lines is a call to the apply method of
some object. For instance, the third line above expands to:
List.apply(1.0, 2.0)
So this is a call to the apply method of the companion object of the List
class. That method takes an arbitrary number of arguments and constructs a
list from them. Every collection class in the Scala library has a companion
object with such an apply method. It does not matter whether the collection
class represents a concrete implementation, like List, Stream, or Vector,
or whether it is an trait such as Seq, Set, or Traversable. In the latter case,
calling apply will produce some default implementation of the trait. Here
are some examples:
scala> List(1, 2, 3)
res17: List[Int] = List(1, 2, 3)
scala> Traversable(1, 2, 3)
res18: Traversable[Int] = List(1, 2, 3)
scala> mutable.Traversable(1, 2, 3)
res19: scala.collection.mutable.Traversable[Int] =
ArrayBuffer(1, 2, 3)
Besides apply, every collection companion object also defines a member
empty, which returns an empty collection. So instead of List() you could
write List.empty, instead of Map(), Map.empty, and so on.
Descendants of Seq traits also provide other factory operations in their
companion objects. These are summarized in Table 24.13. In short, there’s:
concat, which concatenates an arbitrary number of traversables to-
gether,
fill and tabulate, which generate single or multi-dimensional se-
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quences of given dimensions initialized by some expression or tabulating function,
range, which generates integer sequences with some constant step
length, and
iterate, which generates the sequence resulting from repeated appli-
cation of a function to a start element.
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Table 24.13 · Factory methods for sequences
What it is
S.empty
S(x, y, z)
S.concat(xs, ys, zs)
S.fill(n)(e)
The empty sequence
A sequence consisting of elements x, y, and z
The sequence obtained by concatenating the
elements of xs, ys, and zs
A sequence of length n where each element is
computed by expression e
S.fill(m, n)(e)
A sequence of sequences of dimension m × n
where each element is computed by expression e
(exists also in higher dimensions)
S.tabulate(n)(f)
A sequence of length n where the element at each
index i is computed by f(i)
S.tabulate(m, n)(f)
A sequence of sequences of dimension m×n
where the element at each index (i, j) is computed
by f(i, j) (exists also in higher dimensions)
S.range(start, end)
The sequence of integers start . . . end - 1
S.range(start, end, step)
The sequence of integers starting with start and
progressing by step increments up to, and
excluding, the end value
S.iterate(x, n)(f)
The sequence of length n with elements x, f(x),
f(f(x)), . . .
24.17
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What it does
Conversions between Java and Scala collections
Like Scala, Java has a rich collections library. There are many similarities
between the two. For instance, both libraries know iterators, iterables, sets,
maps, and sequences. But there are also important differences. In particular,
the Scala libraries put much more emphasis on immutable collections, and
provide many more operations that transform a collection into a new one.
Sometimes you might need to convert from one collection framework
to the other. For instance, you might want to access to an existing Java
collection, as if it were a Scala collection. Or you might want to pass one
of Scala’s collections to a Java method that expects the Java counterpart. It
is quite easy to do this, because Scala offers implicit conversions between
all the major collection types in the JavaConversions object. In particular,
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you will find bidirectional conversions between the following types:
Iterator
Iterator
Iterable
Iterable
mutable.Buffer
mutable.Set
mutable.Map
⇔
⇔
⇔
⇔
⇔
⇔
⇔
java.util.Iterator
java.util.Enumeration
java.lang.Iterable
java.util.Collection
java.util.List
java.util.Set
java.util.Map
To enable these conversions, simply import like this:
scala> import collection.JavaConversions._
import collection.JavaConversions._
You have now automatic conversions between Scala collections and their
corresponding Java collections.
scala> import collection.mutable._
import collection.mutable._
scala> val jul: java.util.List[Int] = ArrayBuffer(1, 2, 3)
jul: java.util.List[Int] = [1, 2, 3]
scala> val buf: Seq[Int] = jul
buf: scala.collection.mutable.Seq[Int] = ArrayBuffer(1, 2, 3)
scala> val m: java.util.Map[String, Int] =
HashMap("abc" -> 1, "hello" -> 2)
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m: java.util.Map[String,Int] = {hello=2, abc=1}
Internally, these conversion work by setting up a “wrapper” object that forwards all operations to the underlying collection object. So collections are
never copied when converting between Java and Scala. An interesting property is that if you do a round-trip conversion from, say, a Java type to its
corresponding Scala type, and back to the same Java type, you end up with
the identical collection object you started with.
Some other common Scala collections exist that can also be converted
to Java types, but for which no corresponding conversion exists in the other
direction. These are:
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Seq
mutable.Seq
Set
Map
⇒
⇒
⇒
⇒
java.util.List
java.util.List
java.util.Set
java.util.Map
Because Java does not distinguish between mutable and immutable collections in their type, a conversion from, say, collection.immutable.List
will yield a java.util.List, on which all attempted mutation operations
will throw an UnsupportedOperationException. Here’s an example:
scala> val jul: java.util.List[Int] = List(1, 2, 3)
jul: java.util.List[Int] = [1, 2, 3]
scala> jul.add(7)
java.lang.UnsupportedOperationException
at java.util.AbstractList.add(AbstractList.java:131)
24.18
Conclusion
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You’ve now seen how to use Scala’s collection in great detail. Scala’s collections take the approach of giving you powerful building blocks rather than a
number of ad hoc utility methods. Putting together two or three such building
blocks allows you to express an enormous number of useful computations.
This style of library is especially effective due to Scala having a light syntax
for function literals, and due to it providing many collection types that are
persistent and immutable.
This chapter has shown collections from the point of view of a programmer using the collection library. The next chapter will show you how the
collections are built and how you can add your own collection types.
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Chapter 25
The Architecture of Scala Collections
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This chapter describes the architecture of the Scala collections framework in
detail. Continuing the theme of Chapter 24, you will find out more about the
internal workings of the framework. You will also learn how this architecture
helps you define your own collections in a few lines of code, while reusing
the overwhelming part of collection functionality from the framework.
Chapter 24 enumerated a large number of collection operations, which
exist uniformly on many different collection implementations. Implementing
every collection operation anew for every collection type would lead to an
enormous amount of code, most of which would be copied from somewhere
else. Such code duplication could lead to inconsistencies over time, when an
operation is added or modified in one part of the collection library but not in
others. The principal design objective of the new collections framework was
to avoid any duplication, defining every operation in as few places as possible.1 The design approach was to implement most operations in collection
“templates” that can be flexibly inherited from individual base classes and
implementations. In this chapter, we will examine these templates, and other
classes and traits that constitute the “building blocks” of the framework, as
well as the construction principles they support.
25.1
Builders
Almost all collection operations are implemented in terms of traversals and
builders. Traversals are handled by Traversable’s foreach method, and
1 Ideally, everything should be defined in one place only, but there are a few exceptions
where things needed to be redefined.
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Chapter 25 · The Architecture of Scala Collections
building new collections is handled by instances of class Builder. Listing 25.1 presents a slightly abbreviated outline of this class.
package scala.collection.generic
class Builder[-Elem, +To] {
def +=(elem: Elem): this.type
def result(): To
def clear()
def mapResult[NewTo](f: To => NewTo): Builder[Elem, NewTo]
= ...
}
Listing 25.1 · An outline of the Builder class.
You can add an element x to a builder b with b += x. There’s also syntax
to add more than one element at once: For instance, b += (x, y) and b ++= xs
work as for buffers. (In fact, buffers are an enriched version of builders.) The
result() method returns a collection from a builder. The state of the builder
is undefined after taking its result, but it can be reset into a new empty state
using clear(). Builders are generic in both the element type, Elem, and in
the type, To, of collections they return.
Often, a builder can refer to some other builder for assembling the elements of a collection, but then would like to transform the result of the other
builder—for example, to give it a different type. This task is simplified by
method mapResult in class Builder. Suppose for instance you have an
array buffer buf. Array buffers are builders for themselves, so taking the
result() of an array buffer will return the same buffer. If you want to use
this buffer to produce a builder that builds arrays, you can use mapResult:
scala> val buf = new ArrayBuffer[Int]
buf: scala.collection.mutable.ArrayBuffer[Int] = ArrayBuffer()
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scala> val bldr = buf mapResult (_.toArray)
bldr: scala.collection.mutable.Builder[Int,Array[Int]]
= ArrayBuffer()
The result value, bldr, is a builder that uses the array buffer, buf, to collect elements. When a result is demanded from bldr, the result of buf is
computed, which yields the array buffer buf itself. This array buffer is then
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mapped with _.toArray to an array. So the end result is that bldr is a
builder for arrays.
25.2
Factoring out common operations
The main design objectives of the collection library redesign were to have, at
the same time, natural types and maximal sharing of implementation code. In
particular, Scala’s collections follow the “same-result-type” principle: Wherever possible, a transformation method on a collection will yield a collection
of the same type. For instance, the filter operation should yield, on every
collection type, an instance of the same collection type. Applying filter
on a List should give a List; applying it on a Map should give a Map; and
so on. In the rest of this section, you will find out how this is achieved.
The fast track
The material in this section is a bit more dense than usual and might
require some time to absorb. If you want to move ahead quickly, you could
skip the remainder of this section and move on to Section 25.3 on
page 604 where you will learn from concrete examples how to integrate
your own collection classes in the framework.
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The Scala collection library avoids code duplication and achieves the
“same-result-type” principle by using generic builders and traversals over
collections in so-called implementation traits. These traits are named with
a Like suffix; for instance, IndexedSeqLike is the implementation trait for
IndexedSeq, and similarly, TraversableLike is the implementation trait
for Traversable. Collection classes such as Traversable or IndexedSeq
inherit all their concrete method implementations from these traits. Implementation traits have two type parameters instead of one for normal collections. They parameterize not only over the collection’s element type, but
also over the collection’s representation type (i.e., the type of the underlying
collection), such as Seq[I] or List[T].
For instance, here is the header of trait TraversableLike:
trait TraversableLike[+Elem, +Repr] { ... }
The type parameter, Elem, stands for the element type of the traversable
whereas the type parameter Repr stands for its representation. There are
no constraints on Repr. In particular Repr might be instantiated to a type
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package scala.collection
trait TraversableLike[+Elem, +Repr] {
def newBuilder: Builder[Elem, Repr] // deferred
def foreach[U](f: Elem => U)
// deferred
...
def filter(p: Elem => Boolean): Repr = {
val b = newBuilder
foreach { elem => if (p(elem)) b += elem }
b.result
}
}
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Listing 25.2 · Implementation of filter in TraversableLike.
that is itself not a subtype of Traversable. That way, classes outside the
collections hierarchy, such as String and Array, can still make use of all
operations defined in a collection implementation trait.
Taking filter as an example, this operation is defined once for all collection classes in the trait TraversableLike. An outline of the relevant
code is shown in Listing 25.2. The trait declares two abstract methods,
newBuilder and foreach, which are implemented in concrete collection
classes. The filter operation is implemented in the same way for all collections using these methods. It first constructs a new builder for the representation type Repr, using newBuilder. It then traverses all elements of the
current collection, using foreach. If an element x satisfies the given predicate p—i.e., p(x) is true—it is added with the builder. Finally, the elements
collected in the builder are returned as an instance of the Repr collection type
by calling the builder’s result method.
The map operation on collections is a bit more complicated. For instance,
if f is a function from String to Int, and xs is a List[String], then
xs map f should give a List[Int]. Likewise, if ys is an Array[String],
then ys map f should give a Array[Int]. But how do you achieve that
without duplicating the definition of the map method in lists and arrays?
The newBuilder/foreach framework shown in Listing 25.2 is not sufficient for this because it only allows creation of new instances of the same
collection type, whereas map needs an instance of the same collection type
constructor but possibly with a different element type. What’s more, even
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the result type constructor of a function like map might depend, in non-trivial
ways, on the other argument types. Here is an example:
scala> import collection.immutable.BitSet
import collection.immutable.BitSet
scala> val bits = BitSet(1, 2, 3)
bits: scala.collection.immutable.BitSet = BitSet(1, 2, 3)
scala> bits map (_ * 2)
res13: scala.collection.immutable.BitSet = BitSet(2, 4, 6)
scala> bits map (_.toFloat)
res14: scala.collection.immutable.Set[Float] =
Set(1.0, 2.0, 3.0)
If you map the doubling function _ * 2 over a bit set you obtain another bit
set. However, if you map the function (_.toFloat) over the same bit set,
the result is a general Set[Float]. Of course, it can’t be a bit set because
bit sets contain Ints, not Floats.
Note that map’s result type depends on the type of function that’s passed
to it. If the result type of that function argument is again an Int, the result of
map is a BitSet. But if the result type of the function argument is something
else, the result of map is just a Set. You’ll find out soon how this typeflexibility is achieved in Scala.
The problem with BitSet is not an isolated case. Here are two more
interactions with the interpreter that both map a function over a map:
scala> Map("a" -> 1, "b" -> 2) map { case (x, y) => (y, x) }
res3: scala.collection.immutable.Map[Int,java.lang.String] =
Map(1 -> a, 2 -> b)
scala> Map("a" -> 1, "b" -> 2) map { case (x, y) => y }
res4: scala.collection.immutable.Iterable[Int] =
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List(1, 2)
The first function swaps two arguments of a key/value pair. The result of
mapping this function is again a map, but now going in the other direction.
In fact, the first expression yields the inverse of the original map, provided
it is invertible. The second function, however, maps the key/value pair to
an integer, namely its value component. In that case, we cannot form a Map
from the results, but we still can form an Iterable, a supertrait of Map.
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You might ask, Why not restrict map so that it can always return the same
kind of collection? For instance, on bit sets map could accept only Int-toInt functions and on maps it could only accept pair-to-pair functions. Not
only are such restrictions undesirable from an object-oriented modeling point
of view, they are illegal because they would violate the Liskov Substitution
Principle: A Map is an Iterable. So every operation that’s legal on an
Iterable must also be legal on a Map.
Scala solves this problem instead with overloading: Not the simple form
of overloading inherited by Java (that would not be flexible enough), but the
more systematic form of overloading that’s provided by implicit parameters.
def map[B, That](f: Elem => B)
(implicit bf: CanBuildFrom[Repr, B, That]): That = {
val b = bf(this)
for (x <- this) b += f(x)
b.result
}
Listing 25.3 · Implementation of map in TraversableLike.
Listing 25.3 shows trait TraversableLike’s implementation of map. It’s
quite similar to the implementation of filter shown in Listing 25.2. The
principal difference is that where filter used the newBuilder method,
which is abstract in class TraversableLike, map uses a builder factory
that’s passed as an additional implicit parameter of type CanBuildFrom.
package scala.collection.generic
trait CanBuildFrom[-From, -Elem, +To] {
// Creates a new builder
def apply(from: From): Builder[Elem, To]
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}
Listing 25.4 · The CanBuildFrom trait.
Listing 25.4 shows the definition of the trait CanBuildFrom, which represents builder factories. It has three type parameters: Elem indicates the
element type of the collection to be built, To indicates the type of collection
to build, and From indicates the type for which this builder factory applies.
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By defining the right implicit definitions of builder factories, you can tailor
the right typing behavior as needed.
Take class BitSet as an example. Its companion object would contain a
builder factory of type CanBuildFrom[BitSet, Int, BitSet]. This means
that when operating on a BitSet you can construct another BitSet, provided the type of the collection to build is Int. If this is not the case, you can
always fall back to a different implicit builder factory, this time implemented
in mutable.Set’s companion object. The type of this more general builder
factory, where A is a generic type parameter, is:
CanBuildFrom[Set[_], A, Set[A]]
This means that when operating on an arbitrary Set, expressed by the
wildcard type Set[_], you can build a Set again no matter what the element
type A is. Given these two implicit instances of CanBuildFrom, you can then
rely on Scala’s rules for implicit resolution to pick the one that’s appropriate
and maximally specific.
So implicit resolution provides the correct static types for tricky collection operations, such as map. But what about the dynamic types? Specifically, say you have a list value that has Iterable as its static type, and you
map some function over that value:
scala> val xs: Iterable[Int] = List(1, 2, 3)
xs: Iterable[Int] = List(1, 2, 3)
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scala> val ys = xs map (x => x * x)
ys: Iterable[Int] = List(1, 4, 9)
The static type of ys above is Iterable, as expected. But its dynamic type
is (and should be) still List! This behavior is achieved by one more indirection. The apply method in CanBuildFrom is passed the source collection as argument. Most builder factories for generic traversables (in fact
all except builder factories for leaf classes) forward the call to a method
genericBuilder of a collection. The genericBuilder method in turn
calls the builder that belongs to the collection in which it is defined. So
Scala uses static implicit resolution to resolve constraints on the types of
map, and virtual dispatch to pick the best dynamic type that corresponds to
these constraints.
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Chapter 25 · The Architecture of Scala Collections
Integrating new collections
What needs to be done if you want to integrate a new collection class, so that
it can profit from all predefined operations at the right types? In this section
we’ll show you two examples that do this.
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Integrating sequences
Say you want to create a new sequence type for RNA strands, which are
sequences of bases A (adenine), T (thymine), G (guanine), and U (uracil).
The definitions for bases are easily set up as shown in Listing 25.5.
Every base is defined as a case object that inherits from a common abstract class Base. The Base class has a companion object that defines two
functions that map between bases and the integers 0 to 3. You can see in the
examples two different ways to use collections to implement these functions.
The toInt function is implemented as a Map from Base values to integers.
The reverse function, fromInt, is implemented as an array. This makes use
of the fact that both maps and arrays are functions because they inherit from
the Function1 trait.
The next task is to define a class for strands of RNA. Conceptually, a
strand of RNA is simply a Seq[Base]. However, RNA strands can get quite
long, so it makes sense to invest some work in a compact representation.
Because there are only four bases, a base can be identified with two bits, and
you can therefore store sixteen bases as two-bit values in an integer. The
idea, then, is to construct a specialized subclass of Seq[Base], which uses
this packed representation.
Listing 25.6 presents the first version of this class; it will be refined later.
The class RNA1 has a constructor that takes an array of Ints as its first argument. This array contains the packed RNA data, with sixteen bases in each
element, except for the last array element, which might be partially filled.
The second argument, length, specifies the total number of bases on the
array (and in the sequence). Class RNA1 extends IndexedSeq[Base]. Trait
IndexedSeq, which comes from package scala.collection.immutable,
defines two abstract methods, length and apply.
These need to be implemented in concrete subclasses. Class RNA1 implements length automatically by defining a parametric field (described in
Section 10.6) of the same name. It implements the indexing method apply
with the code given in Listing 25.6. Essentially, apply first extracts an inCover · Overview · Contents · Discuss · Suggest · Glossary · Index
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abstract class Base
case object A extends Base
case object T extends Base
case object G extends Base
case object U extends Base
object Base {
val fromInt: Int => Base = Array(A, T, G, U)
val toInt: Base => Int = Map(A -> 0, T -> 1, G -> 2, U -> 3)
}
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Listing 25.5 · RNA Bases.
teger value from the groups array, then extracts the correct two-bit number
from that integer using right shift (>>) and mask (&). The private constants S,
N, and M come from the RNA1 companion object. S specifies the size of each
packet (i.e., two); N specifies the number of two-bit packets per integer; and
M is a bit mask that isolates the lowest S bits in a word.
Note that the constructor of class RNA1 is private. This means that
clients cannot create RNA1 sequences by calling new, which makes sense,
because it hides the representation of RNA1 sequences in terms of packed
arrays from the user. If clients cannot see what the representation details of
RNA sequences are, it becomes possible to change these representation details
at any point in the future without affecting client code.
In other words, this design achieves a good decoupling of the interface of
RNA sequences and its implementation. However, if constructing an RNA
sequence with new is impossible, there must be some other way to create
new RNA sequences, or else the whole class would be rather useless. There
are two alternatives for RNA sequence creation, both provided by the RNA1
companion object. The first way is method fromSeq, which converts a given
sequence of bases (i.e., a value of type Seq[Base]) into an instance of class
RNA1. The fromSeq method does this by packing all the bases contained in
its argument sequence into an array, then calling RNA1’s private constructor
with that array and the length of the original sequence as arguments. This
makes use of the fact that a private constructor of a class is visible in the
class’s companion object.
The second way to create an RNA1 value is provided by the apply method
in the RNA1 object. It takes a variable number of Base arguments and simply
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import collection.IndexedSeqLike
import collection.mutable.{Builder, ArrayBuffer}
import collection.generic.CanBuildFrom
final class RNA1 private (val groups: Array[Int],
val length: Int) extends IndexedSeq[Base] {
import RNA1._
def apply(idx: Int): Base = {
if (idx < 0 || length <= idx)
throw new IndexOutOfBoundsException
Base.fromInt(groups(idx / N) >> (idx % N * S) & M)
}
}
object RNA1 {
// Number of bits necessary to represent group
private val S = 2
// Number of groups that fit in an Int
private val N = 32 / S
// Bitmask to isolate a group
private val M = (1 << S) - 1
def fromSeq(buf: Seq[Base]): RNA1 = {
val groups = new Array[Int]((buf.length + N - 1) / N)
for (i <- 0 until buf.length)
groups(i / N) |= Base.toInt(buf(i)) << (i % N * S)
new RNA1(groups, buf.length)
}
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def apply(bases: Base*) = fromSeq(bases)
}
Listing 25.6 · RNA strands class, first version.
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forwards them as a sequence to fromSeq.
Here are the two creation schemes in action:
scala> val xs = List(A, G, T, A)
xs: List[Product with Base] = List(A, G, T, A)
scala> RNA1.fromSeq(xs)
res1: RNA1 = RNA1(A, G, T, A)
scala> val rna1 = RNA1(A, U, G, G, T)
rna1: RNA1 = RNA1(A, U, G, G, T)
Adapting the result type of RNA methods
Here are some more interactions with the RNA1 abstraction:
scala> rna1.length
res2: Int = 5
scala> rna1.last
res3: Base = T
scala> rna1.take(3)
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res4: IndexedSeq[Base] = Vector(A, U, G)
The first two results are as expected, but the last result of taking the first three
elements of rna1 might not be. In fact, you see an IndexedSeq[Base] as
static result type and a Vector as the dynamic type of the result value. You
might have expected to see an RNA1 value instead. But this is not possible because all that was done in Listing 25.6 was make RNA1 extend IndexedSeq.
Class IndexedSeq, on the other hand, has a take method that returns an
IndexedSeq, and that’s implemented in terms of IndexedSeq’s default implementation, Vector.
Now that you understand why things are the way they are, the next question should be what needs to be done to change them? One way to do this
would be to override the take method in class RNA1, maybe like this:
def take(count: Int): RNA1 = RNA1.fromSeq(super.take(count))
This would do the job for take. But what about drop, or filter, or init?
In fact there are over fifty methods on sequences that return again a sequence.
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For consistency, all of these would have to be overridden. This looks less and
less like an attractive option.
Fortunately, there is a much easier way to achieve the same effect. The
RNA class needs to inherit not only from IndexedSeq, but also from its
implementation trait IndexedSeqLike. This is shown in Listing 25.7. The
new implementation differs from the previous one in only two aspects. First,
class RNA2 now also extends from IndexedSeqLike[Base, RNA2]. The
IndexedSeqLike trait implements all concrete methods of IndexedSeq in
an extensible way.
For instance, the return type of methods like take, drop, filter or
init is the second type parameter passed to class IndexedSeqLike (i.e.,
RNA2 in Listing 25.7). To do this, IndexedSeqLike bases itself on the
newBuilder abstraction, which creates a builder of the right kind. Subclasses of trait IndexedSeqLike have to override newBuilder to return collections of their own kind. In class RNA2, the newBuilder method returns a
builder of type Builder[Base, RNA2]. To construct this builder, it first creates an ArrayBuffer, which itself is a Builder[Base, ArrayBuffer]. It
then transforms the ArrayBuffer builder by calling its mapResult method
to an RNA2 builder. The mapResult method expects a transformation function from ArrayBuffer to RNA2 as its parameter. The function given is simply RNA2.fromSeq, which converts an arbitrary base sequence to an RNA2
value (recall that an array buffer is a kind of sequence, so RNA2.fromSeq
can be applied to it).
final class RNA2 private (
val groups: Array[Int],
val length: Int
) extends IndexedSeq[Base] with IndexedSeqLike[Base, RNA2] {
import RNA2._
override def newBuilder: Builder[Base, RNA2] =
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new ArrayBuffer[Base] mapResult fromSeq
def apply(idx: Int): Base = // as before
}
Listing 25.7 · RNA strands class, second version.
If you had left out the newBuilder definition, you would have gotten an
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error message like the following:
RNA2.scala:5:
error:
overriding method newBuilder in trait
TraversableLike of type => scala.collection.mutable.Builder[Base,RNA2];
method newBuilder in trait GenericTraversableTemplate of type
=> scala.collection.mutable.Builder[Base,IndexedSeq[Base]] has
incompatible type
class RNA2 private (val groups:
Array[Int], val length:
Int)
ˆ
one error found
The error message is quite long and complicated, which reflects the intricate
way the collection libraries are put together. It’s best to ignore the information about where the methods come from, because in this case it detracts
more than it helps. What remains is that a method newBuilder with result
type Builder[Base, RNA2] needed to be defined, but a method newBuilder
with result type Builder[Base,IndexedSeq[Base]] was found. The latter
does not override the former.
The first method, whose result type is Builder[Base, RNA2], is an abstract method that got instantiated at this type in Listing 25.7 by passing
the RNA2 type parameter to IndexedSeqLike. The second method, of result
type Builder[Base,IndexedSeq[Base]], is what’s provided by the inherited IndexedSeq class. In other words, the RNA2 class is invalid without a
definition of newBuilder with the first result type.
With the refined implementation of the RNA class in Listing 25.7, methods like take, drop, or filter work now as expected:
scala> val rna2 = RNA2(A, U, G, G, T)
rna2: RNA2 = RNA2(A, U, G, G, T)
scala> rna2 take 3
res5: RNA2 = RNA2(A, U, G)
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scala> rna2 filter (U !=)
res6: RNA2 = RNA2(A, G, G, T)
Dealing with map and friends
There is another class of methods in collections that we haven’t dealt with
yet. These methods do not always return the collection type exactly. They
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might return the same kind of collection, but with a different element type.
The classical example of this is the map method. If s is a Seq[Int], and f is a
function from Int to String, then s.map(f) would return a Seq[String].
So the element type changes between the receiver and the result, but the kind
of collection stays the same.
There are a number of other methods that behave like map. For some
of them you would expect this (e.g., flatMap, collect), but for others you
might not. For instance, the append method, ++, also might return a result
whose type differs from that of its arguments—appending a list of String to
a list of Int would give a list of Any. How should these methods be adapted
to RNA strands? Ideally we’d expect that mapping bases to bases over an
RNA strand would yield again an RNA strand:
scala> val rna = RNA(A, U, G, G, T)
rna: RNA = RNA(A, U, G, G, T)
scala> rna map { case A => T case b => b }
res7: RNA = RNA(T, U, G, G, T)
Likewise, appending two RNA strands with ++ should yield again another
RNA strand:
scala> rna ++ rna
res8: RNA = RNA(A, U, G, G, T, A, U, G, G, T)
On the other hand, mapping bases to some other type over an RNA strand
cannot yield another RNA strand because the new elements have the wrong
type. It has to yield a sequence instead. In the same vein appending elements
that are not of type Base to an RNA strand can yield a general sequence, but
it cannot yield another RNA strand.
scala> rna map Base.toInt
res2: IndexedSeq[Int] = Vector(0, 3, 2, 2, 1)
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scala> rna ++ List("missing", "data")
res3: IndexedSeq[java.lang.Object] =
Vector(A, U, G, G, T, missing, data)
This is what you’d expect in the ideal case. But this is not what the RNA2 class
as given in Listing 25.7 provides. In fact, if you ran the first two examples
above with instances of this class you would obtain:
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scala> val rna2 = RNA2(A, U, G, G, T)
rna2: RNA2 = RNA2(A, U, G, G, T)
scala> rna2 map { case A => T case b => b }
res0: IndexedSeq[Base] = Vector(T, U, G, G, T)
scala> rna2 ++ rna2
res1: IndexedSeq[Base] = Vector(A, U, G, G, T, A, U, G, G, T)
So the result of map and ++ is never an RNA strand, even if the element
type of the generated collection is a Base. To see how to do better, it
pays to have a close look at the signature of the map method (or of ++,
which has a similar signature). The map method is originally defined in class
scala.collection.TraversableLike with the following signature:
def map[B, That](f: Elem => B)
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(implicit cbf: CanBuildFrom[Repr, B, That]): That
Here Elem is the type of elements of the collection, and Repr is the type of
the collection itself; that is, the second type parameter that gets passed to implementation classes such as TraversableLike and IndexedSeqLike. The
map method takes two more type parameters, B and That. The B parameter
stands for the result type of the mapping function, which is also the element
type of the new collection. The That appears as the result type of map, so it
represents the type of the new collection that gets created.
How is the That type determined? It is linked to the other types by
an implicit parameter cbf, of type CanBuildFrom[Repr, B, That]. These
CanBuildFrom implicits are defined by the individual collection classes. In
essence, an implicit value of type CanBuildFrom[From, Elem, To] says:
“Here is a way, given a collection of type From, to build with elements of
type Elem a collection of type To.”
Now the behavior of map and ++ on RNA2 sequences becomes clearer.
There is no CanBuildFrom instance that creates RNA2 sequences, so the next
best available CanBuildFrom was found in the companion object of the inherited trait IndexedSeq. That implicit creates IndexedSeqs, and that’s
what you saw when applying map to rna2.
To address this shortcoming, you need to define an implicit instance of
CanBuildFrom in the companion object of the RNA class. That instance
should have type CanBuildFrom[RNA, Base, RNA]. Hence, this instance
states that, given an RNA strand and a new element type Base, you can
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final class RNA private (val groups: Array[Int], val length: Int)
extends IndexedSeq[Base] with IndexedSeqLike[Base, RNA] {
import RNA._
// Mandatory re-implementation of ‘newBuilder‘ in ‘IndexedSeq‘
override protected[this] def newBuilder: Builder[Base, RNA] =
RNA.newBuilder
// Mandatory implementation of ‘apply‘ in ‘IndexedSeq‘
def apply(idx: Int): Base = {
if (idx < 0 || length <= idx)
throw new IndexOutOfBoundsException
Base.fromInt(groups(idx / N) >> (idx % N * S) & M)
}
// Optional re-implementation of foreach,
// to make it more efficient.
override def foreach[U](f: Base => U): Unit = {
var i = 0
var b = 0
while (i < length) {
b = if (i % N == 0) groups(i / N) else b >>> S
f(Base.fromInt(b & M))
i += 1
}
}
}
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Listing 25.8 · RNA strands class, final version.
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build another collection which is again an RNA strand. Listing 25.8 and
Listing 25.9 show the details.
object RNA {
private val S = 2
// number of bits in group
private val M = (1 << S) - 1 // bitmask to isolate a group
private val N = 32 / S
// number of groups in an Int
def fromSeq(buf: Seq[Base]): RNA = {
val groups = new Array[Int]((buf.length + N - 1) / N)
for (i <- 0 until buf.length)
groups(i / N) |= Base.toInt(buf(i)) << (i % N * S)
new RNA(groups, buf.length)
}
def apply(bases: Base*) = fromSeq(bases)
def newBuilder: Builder[Base, RNA] =
new ArrayBuffer mapResult fromSeq
implicit def canBuildFrom: CanBuildFrom[RNA, Base, RNA] =
new CanBuildFrom[RNA, Base, RNA] {
def apply(): Builder[Base, RNA] = newBuilder
def apply(from: RNA): Builder[Base, RNA] = newBuilder
}
}
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Listing 25.9 · RNA companion object—final version.
Compared to class RNA2 there are two important differences. First, the
newBuilder implementation has moved from the RNA class to its companion object. The newBuilder method in class RNA simply forwards to this
definition. Second, there is now an implicit CanBuildFrom value in object
RNA. To create such an object you need to define two apply methods in the
CanBuildFrom trait. Both create a new builder for an RNA collection, but
they differ in their argument list. The apply() method simply creates a new
builder of the right type. By contrast, the apply(from) method takes the
original collection as argument. This can be useful to adapt the dynamic type
of builder’s return type to be the same as the dynamic type of the receiver. In
the case of RNA this does not come into play because RNA is a final class, so
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any receiver of static type RNA also has RNA as its dynamic type. That’s why
apply(from) also simply calls newBuilder, ignoring its argument.
That is it. The RNA class in Listing 25.8 implements all collection methods at their natural types. Its implementation requires a little bit of protocol.
In essence, you need to know where to put the newBuilder factories and the
canBuildFrom implicits. On the plus side, with relatively little code you get
a large number of methods automatically defined. Also, if you don’t intend
to do bulk operations like take, drop, map, or ++ on your collection, you can
choose to not go the extra length and stop at the implementation shown in
Listing 25.6.
The discussion so far centered on the minimal amount of definitions
needed to define new sequences with methods that obey certain types. But
in practice you might also want to add new functionality to your sequences
or override existing methods for better efficiency. An example of this is the
overridden foreach method in class RNA. foreach is an important method
in its own right because it implements loops over collections. Furthermore,
many other collection methods are implemented in terms of foreach. So it
makes sense to invest some effort optimizing the method’s implementation.
The standard implementation of foreach in IndexedSeq will simply select every i’th element of the collection using apply, where i ranges from 0
to the collection’s length minus one. So this standard implementation selects
an array element and unpacks a base from it once for every element in an
RNA strand. The overriding foreach in class RNA is smarter than that. For
every selected array element it immediately applies the given function to all
bases contained in it. So the effort for array selection and bit unpacking is
much reduced.
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Integrating new sets and maps
As a second example you’ll learn how to integrate a new kind of map into the
collection framework. The idea is to implement a mutable map with String
as the type of keys by a “Patricia trie”.2 The term Patricia is an abbreviation
for “Practical Algorithm to Retrieve Information Coded in Alphanumeric.”
The idea is to store a set or a map as a tree where subsequent characters in a
search key determines uniquely a descendant tree.
2 Morrison, “PATRICIA—Practical Algorithm To Retrieve Information Coded in Alphanumeric” [Mor68]
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a
y
l
b
c
x
d
l
Figure 25.1 · An example Patricia trie.
For instance, a Patricia trie storing the five strings, "abc", "abd", "al",
"all", "xy", would look like the tree given in Figure 25.1. To find the node
corresponding to the string "abc" in this trie, simply follow the subtree labeled "a", proceed from there to the subtree labeled "b" to finally reach its
subtree labeled "c". If the Patricia trie is used as a map, the value that’s associated with a key is stored in the nodes that can be reached by the key. If it
is a set, you simply store a marker saying that the node is present in the set.
Patricia tries support very efficient lookups and updates. Another nice
feature is that they support selecting a subcollection by giving a prefix. For
instance, in the tree in Figure 25.1 you can obtain the sub-collection of all
keys that start with an "a" simply by following the "a" link from the root of
the tree.
Based on these ideas we will now walk you through the implementation
of a map that’s implemented as a Patricia trie. We call the map a PrefixMap,
which means that it provides a method withPrefix that selects a submap of
all keys starting with a given prefix.
We’ll first define a prefix map with the keys shown in Figure 25.1:
scala> val m = PrefixMap("abc" -> 0, "abd" -> 1, "al" -> 2,
"all" -> 3, "xy" -> 4)
m: PrefixMap[Int] = Map((abc,0), (abd,1), (al,2), (all,3),
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(xy,4))
Then calling withPrefix on m will yield another prefix map:
scala> m withPrefix "a"
res14: PrefixMap[Int] = Map((bc,0), (bd,1), (l,2), (ll,3))
Listing 25.10 shows the definition of PrefixMap. This class is parameterized
with the type of associated values T, and extends mutable.Map[String, T]
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import collection._
class PrefixMap[T]
extends mutable.Map[String, T]
with mutable.MapLike[String, T, PrefixMap[T]] {
var suffixes: immutable.Map[Char, PrefixMap[T]] = Map.empty
var value: Option[T] = None
def get(s: String): Option[T] =
if (s.isEmpty) value
else suffixes get (s(0)) flatMap (_.get(s substring 1))
def withPrefix(s: String): PrefixMap[T] =
if (s.isEmpty) this
else {
val leading = s(0)
suffixes get leading match {
case None =>
suffixes = suffixes + (leading -> empty)
case _ =>
}
suffixes(leading) withPrefix (s substring 1)
}
override def update(s: String, elem: T) =
withPrefix(s).value = Some(elem)
override def remove(s: String): Option[T] =
if (s.isEmpty) { val prev = value; value = None; prev }
else suffixes get (s(0)) flatMap (_.remove(s substring 1))
def iterator: Iterator[(String, T)] =
(for (v <- value.iterator) yield ("", v)) ++
(for ((chr, m) <- suffixes.iterator;
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(s, v) <- m.iterator) yield (chr +: s, v))
def += (kv: (String, T)): this.type = { update(kv._1, kv._2); this }
def -= (s: String): this.type
= { remove(s); this }
override def empty = new PrefixMap[T]
}
Listing 25.10 · An implementation of prefix maps with Patricia tries.
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and mutable.MapLike[String, T, PrefixMap[T]]. You have seen this
pattern already for sequences in the RNA strand example. Then as now
inheriting an implementation class such as MapLike serves to get the right
result type for transformations such as filter.
A prefix map node has two mutable fields: suffixes and value. The
value field contains an optional value that’s associated with the node. It is
initialized to None. The suffixes field contains a map from characters to
PrefixMap values. It is initialized to the empty map. You might ask, Why
did we pick an immutable map as the implementation type for suffixes?
Would not a mutable map been more standard since PrefixMap as a whole
is also mutable? The answer is that immutable maps that contain only a few
elements are very efficient in both space and execution time.
For instance, maps that contain fewer than 5 elements are represented
as a single object. By contrast, as described in Section 17.2, the standard
mutable map is a HashMap, which typically occupies around 80 bytes, even
if it is empty. So if small collections are common, it’s better to pick immutable over mutable. In the case of Patricia tries, we’d expect that most
nodes, except the ones at the very top of the tree, would contain only a few
successors. So storing these successors in an immutable map is likely to be
more efficient.
Now have a look at the first method that needs to be implemented for
a map: get. The algorithm is as follows: To get the value associated with
the empty string in a prefix map, simply select the optional value stored in
the root of the tree. Otherwise, if the key string is not empty, try to select
the submap corresponding to the first character of the string. If that yields
a map, follow up by looking up the remainder of the key string after its
first character in that map. If the selection fails, the key is not stored in the
map, so return with None. The combined selection over an option value is
elegantly expressed using flatMap. When applied to an optional value, ov,
and a closure, f, which in turn returns an optional value, ov flatMap f will
succeed if both ov and f return a defined value. Otherwise ov flatMap f
will return None.
The next two methods to implement for a mutable map are += and -=. In
the implementation of Listing 25.10, these are defined in terms of two other
methods: update and remove. The remove method is very similar to get,
except that before returning any associated value, the field containing that
value is set to None. The update method first calls withPrefix to navigate
to the tree node that needs to be updated, then sets the value field of that
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node to the given value. The withPrefix method navigates through the
tree, creating sub-maps as necessary if some prefix of characters is not yet
contained as a path in the tree.
The last abstract method to implement for a mutable map is iterator.
This method needs to produce an iterator that yields all key/value pairs stored
in the map. For any given prefix map this iterator is composed of the following parts: First, if the map contains a defined value, Some(x), in the value
field at its root, then ("", x) is the first element returned from the iterator.
Furthermore, the iterator needs to traverse the iterators of all submaps stored
in the suffixes field, but it needs to add a character in front of every key
string returned by those iterators. More precisely, if m is the submap reached
from the root through a character chr, and (s, v) is an element returned
from m.iterator, then the root’s iterator will return (chr +: s, v) instead.
This logic is implemented quite concisely as a concatenation of two for
expressions in the implementation of the iterator method in Listing 25.10.
The first for expression iterates over value.iterator. This makes use of
the fact that Option values define an iterator method that returns either no
element, if the option value is None, or exactly one element x, if the option
value is Some(x).
Note that there is no newBuilder method defined in PrefixMap. There
is no need because maps and sets come with default builders, which are
instances of class MapBuilder. For a mutable map the default builder starts
with an empty map and then adds successive elements using the map’s +=
method. Mutable sets behave the same. The default builders for immutable
maps and sets use the non-destructive element addition method +, instead of
method +=. However, in all these cases, to build the right kind of set or map,
you need to start with an empty set or map of this kind. This is provided
by the empty method, which is the last method defined in PrefixMap. In
Listing 25.10, this method simply returns a fresh PrefixMap.
We’ll now turn to the companion object PrefixMap, which is shown in
Listing 25.11. In fact it is not strictly necessary to define this companion
object, as class PrefixMap can stand well on its own. The main purpose of
object PrefixMap is to define some convenience factory methods. It also
defines a CanBuildFrom implicit to make typing work out better.
The two convenience methods are empty and apply. The same methods
are present for all other collections in Scala’s collection framework so it
makes sense to define them here too. With the two methods, you can write
PrefixMap literals like you do for any other collection:
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import scala.collection.mutable.{Builder, MapBuilder}
import scala.collection.generic.CanBuildFrom
object PrefixMap {
def empty[T] = new PrefixMap[T]
def apply[T](kvs: (String, T)*): PrefixMap[T] = {
val m: PrefixMap[T] = empty
for (kv <- kvs) m += kv
m
}
def newBuilder[T]: Builder[(String, T), PrefixMap[T]] =
new MapBuilder[String, T, PrefixMap[T]](empty)
implicit def canBuildFrom[T]
: CanBuildFrom[PrefixMap[_], (String, T), PrefixMap[T]] =
new CanBuildFrom[PrefixMap[_], (String, T), PrefixMap[T]] {
def apply(from: PrefixMap[_]) = newBuilder[T]
def apply() = newBuilder[T]
}
}
Listing 25.11 · The companion object for prefix maps.
scala> PrefixMap("hello" -> 5, "hi" -> 2)
res0: PrefixMap[Int] = Map((hello,5), (hi,2))
scala> PrefixMap.empty[String]
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res2: PrefixMap[String] = Map()
The other member in object PrefixMap is an implicit CanBuildFrom instance. It has the same purpose as the CanBuildFrom definition in the last
section: to make methods like map return the best possible type. For instance,
consider mapping a function over the key/value pairs of a PrefixMap. As
long as that function produces pairs of strings and some second type, the
result collection will again be a PrefixMap. Here’s an example:
scala> res0 map { case (k, v) => (k + "!", "x" * v) }
res8: PrefixMap[String] = Map((hello!,xxxxx), (hi!,xx))
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The given function argument takes the key/value bindings of the prefix map
res0 and produces pairs of strings. The result of the map is a PrefixMap,
this time with value type String instead of Int. Without the canBuildFrom
implicit in PrefixMap the result would just have been a general mutable
map, not a prefix map.
Summary
If you want to fully integrate a new collection class into the framework, you
need to pay attention to the following points:
1. Decide whether the collection should be mutable or immutable.
2. Pick the right base traits for the collection.
3. Inherit from the right implementation trait to implement most collection operations.
4. If you want map and similar operations to return instances of your collection type, provide an implicit CanBuildFrom in your class’s companion object.
25.4
Conclusion
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You have now seen how Scala’s collections are built and how you can build
new kinds of collections. Because of Scala’s rich support for abstraction,
each new collection type can have a large number of methods without having
to reimplement them all over again.
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620
Chapter 26
Extractors
By now you have probably grown accustomed to the concise way data can be
decomposed and analyzed using pattern matching. This chapter shows you
how to generalize this concept further. Until now, constructor patterns were
linked to case classes. For instance, Some(x) is a valid pattern because Some
is a case class. Sometimes you might wish that you could write patterns like
this without creating an associated case class. In fact, you might wish to be
able to create your own kinds of patterns. Extractors give you a way to do
so. This chapter explains what extractors are and how you can use them to
define patterns that are decoupled from an object’s representation.
26.1
An example: extracting email addresses
To illustrate the problem extractors solve, imagine that you need to analyze
strings that represent email addresses. Given a string, you want to decide
whether it is an email address or not, and, if it is, you want to access the user
and domain parts of the address. The traditional way to do this uses three
helper functions:
def isEMail(s: String): Boolean
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def domain(s: String): String
def user(s: String): String
With these functions, you could parse a given string s as follows:
if (isEMail(s)) println(user(s) + " AT " + domain(s))
else println("not an email address")
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Section 26.2
Chapter 26 · Extractors
This works, but is kind of clumsy. What’s more, things would become more
complicated if you combined several such tests. For instance you might want
to find two successive strings in a list that are both email addresses with
the same user. You can try this yourself with the access functions defined
previously to see what would be involved.
You saw already in Chapter 15 that pattern matching is ideal for attacking
problems like this. Let’s assume for the moment that you could match a
string with a pattern:
EMail(user, domain)
The pattern would match if the string contained an embedded at sign (@). In
that case it would bind variable user to the part of the string before the @
and variable domain to the part after it. Postulating a pattern like this, the
previous expression could be written more clearly like this:
s match {
case EMail(user, domain) => println(user + " AT " + domain)
case _ => println("not an email address")
}
The more complicated problem of finding two successive email addresses
with the same user part would translate to the following pattern:
ss match {
case EMail(u1, d1) :: EMail(u2, d2) :: _ if (u1 == u2) => ...
...
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}
This is much more legible than anything that could be written with access
functions. However, the problem is that strings are not case classes; they do
not have a representation that conforms to EMail(user, domain). This is
where Scala’s extractors come in: they let you define new patterns for preexisting types, where the pattern need not follow the internal representation
of the type.
26.2
Extractors
An extractor in Scala is an object that has a method called unapply as one of
its members. The purpose of that unapply method is to match a value and
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take it apart. Often, the extractor object also defines a dual method apply for
building values, but this is not required. As an example, Listing 26.1 shows
an extractor object for email addresses:
object EMail {
// The injection method (optional)
def apply(user: String, domain: String) = user + "@" + domain
// The extraction method (mandatory)
def unapply(str: String): Option[(String, String)] = {
val parts = str split "@"
if (parts.length == 2) Some(parts(0), parts(1)) else None
}
}
Listing 26.1 · The EMail string extractor object.
This object defines both apply and unapply methods. The apply method
has the same meaning as always: it turns EMail into an object that can
be applied to arguments in parentheses in the same way a method is applied. So you can write EMail("John", "epfl.ch") to construct the string
"John@epfl.ch". To make this more explicit, you could also let EMail
inherit from Scala’s function type, like this:
object EMail extends ((String, String) => String) { ... }
Note
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The “(String, String) => String” portion of the previous object
declaration means the same as Function2[String, String, String],
which declares an abstract apply method that EMail implements. As a
result of this declaration, you could, for example, pass EMail to a method
expecting a Function2[String, String, String].
The unapply method is what turns EMail into an extractor. In a sense, it
reverses the construction process of apply. Where apply takes two strings
and forms an email address string out of them, unapply takes an email address and returns potentially two strings: the user and the domain of the address. But unapply must also handle the case where the given string is not
an email address. That’s why unapply returns an Option-type over pairs of
strings. Its result is either Some(user, domain) if the string str is an email
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address with the given user and domain parts,1 or None, if str is not an
email address. Here are some examples:
equals Some("John", "epfl.ch")
equals None
unapply("John@epfl.ch")
unapply("John Doe")
Now, whenever pattern matching encounters a pattern referring to an extractor object, it invokes the extractor’s unapply method on the selector expression. For instance, executing the code:
selectorString match { case EMail(user, domain) => ... }
would lead to the call:
EMail.unapply(selectorString)
As you saw previously, this call to EMail.unapply will return either None
or Some(u, d), for some values u for the user part of the address and d
for the domain part. In the None case, the pattern does not match, and the
system tries another pattern or fails with a MatchError exception. In the
Some(u, d) case, the pattern matches and its variables are bound to the elements of the returned value. In the previous match, user would be bound to
u and domain would be bound to d.
In the EMail pattern matching example, the type String of the selector expression, selectorString, conformed to unapply’s argument type
(which in the example was also String). This is quite common, but not
necessary. It would also be possible to use the EMail extractor to match selector expressions for more general types. For instance, to find out whether
an arbitrary value x was an email address string, you could write:
val x: Any = ...
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x match { case EMail(user, domain) => ... }
Given this code, the pattern matcher will first check whether the given value
x conforms to String, the parameter type of EMail’s unapply method. If it
does conform, the value is cast to String and pattern matching proceeds as
before. If it does not conform, the pattern fails immediately.
1 As
demonstrated here, where Some is applied to the tuple, (user, domain), you can
leave off one pair of parentheses when passing a tuple to a function that takes a single argument. Thus, Some(user, domain) means the same as Some((user, domain)).
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In object EMail, the apply method is called an injection, because it takes
some arguments and yields an element of a given set (in our case: the set of
strings that are email addresses). The unapply method is called an extraction, because it takes an element of the same set and extracts some of its parts
(in our case: the user and domain substrings). Injections and extractions are
often grouped together in one object, because then you can use the object’s
name for both a constructor and a pattern, which simulates the convention
for pattern matching with case classes. However, it is also possible to define
an extraction in an object without a corresponding injection. The object itself
is called an extractor, regardless of whether or not it has an apply method.
If an injection method is included, it should be the dual to the extraction
method. For instance, a call of:
EMail.unapply(EMail.apply(user, domain))
should return:
Some(user, domain)
i.e., the same sequence of arguments wrapped in a Some. Going in the other
direction means running first the unapply and then the apply, as shown in
the following code:
EMail.unapply(obj) match {
case Some(u, d) => EMail.apply(u, d)
}
In that code, if the match on obj succeeds, you’d expect to get back that
same object from the apply. These two conditions for the duality of apply
and unapply are good design principles. They are not enforced by Scala,
but it’s recommended to keep to them when designing your extractors.
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26.3
Patterns with zero or one variables
The unapply method of the previous example returned a pair of element
values in the success case. This is easily generalized to patterns of more than
two variables. To bind N variables, an unapply would return an N-element
tuple, wrapped in a Some.
The case where a pattern binds just one variable is treated differently,
however. There is no one-tuple in Scala. To return just one pattern element,
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the unapply method simply wraps the element itself in a Some. For example,
the extractor object shown in Listing 26.2 defines apply and unapply for
strings that consist of the same substring appearing twice in a row:
object Twice {
def apply(s: String): String = s + s
def unapply(s: String): Option[String] = {
val length = s.length / 2
val half = s.substring(0, length)
if (half == s.substring(length)) Some(half) else None
}
}
Listing 26.2 · The Twice string extractor object.
It’s also possible that an extractor pattern does not bind any variables. In
that case the corresponding unapply method returns a boolean—true for
success and false for failure. For instance, the extractor object shown in
Listing 26.3 characterizes strings consisting of all uppercase characters:
object UpperCase {
def unapply(s: String): Boolean = s.toUpperCase == s
}
Listing 26.3 · The UpperCase string extractor object.
This time, the extractor only defines an unapply, but not an apply. It would
make no sense to define an apply, as there’s nothing to construct.
The following userTwiceUpper function applies all previously defined
extractors together in its pattern matching code:
def userTwiceUpper(s: String) = s match {
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case EMail(Twice(x @ UpperCase()), domain) =>
"match: " + x + " in domain " + domain
case _ =>
"no match"
}
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The first pattern of this function matches strings that are email addresses
whose user part consists of two occurrences of the same string in uppercase
letters. For instance:
scala> userTwiceUpper("DIDI@hotmail.com")
res0: String = match: DI in domain hotmail.com
scala> userTwiceUpper("DIDO@hotmail.com")
res1: String = no match
scala> userTwiceUpper("didi@hotmail.com")
res2: String = no match
Note that UpperCase in function userTwiceUpper takes an empty parameter list. This cannot be omitted as otherwise the match would test for equality
with the object UpperCase! Note also that, even though UpperCase() itself
does not bind any variables, it is still possible to associate a variable with
the whole pattern matched by it. To do this, you use the standard scheme
of variable binding explained in Section 15.2: the form x @ UpperCase()
associates the variable x with the pattern matched by UpperCase(). For
instance, in the first userTwiceUpper invocation above, x was bound to
"DI", because that was the value against which the UpperCase() pattern
was matched.
26.4
Variable argument extractors
The previous extraction methods for email addresses all returned a fixed
number of element values. Sometimes, this is not flexible enough. For example, you might want to match on a string representing a domain name, so
that every part of the domain is kept in a different sub-pattern. This would
let you express patterns such as the following:
dom match {
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case Domain("org", "acm") => println("acm.org")
case Domain("com", "sun", "java") => println("java.sun.com")
case Domain("net", _*) => println("a .net domain")
}
In this example things were arranged so that domains are expanded in reverse order—from the top-level domain down to the sub-domains. This was
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done so that you could better profit from sequence patterns. You saw in Section 15.2 that a sequence wildcard pattern, _*, at the end of an argument list
matches any remaining elements in a sequence. This feature is more useful if the top-level domain comes first, because then you can use sequence
wildcards to match sub-domains of arbitrary depth.
The question remains how an extractor can support vararg matching as
shown in the previous example, where patterns can have a varying number of
sub-patterns. The unapply methods encountered so far are not sufficient, because they each return a fixed number of sub-elements in the success case. To
handle this case, Scala lets you define a different extraction method specifically for vararg matching. This method is called unapplySeq. To see how it
is written, have a look at the Domain extractor, shown in Listing 26.4:
object Domain {
// The injection method (optional)
def apply(parts: String*): String =
parts.reverse.mkString(".")
// The extraction method (mandatory)
def unapplySeq(whole: String): Option[Seq[String]] =
Some(whole.split("\\.").reverse)
}
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Listing 26.4 · The Domain string extractor object.
The Domain object defines an unapplySeq method that first splits the string
into parts separated by periods. This is done using Java’s split method on
strings, which takes a regular expression as its argument. The result of split
is an array of substrings. The result of unapplySeq is then that array with
all elements reversed and wrapped in a Some.
The result type of an unapplySeq must conform to Option[Seq[T]],
where the element type T is arbitrary. As you saw in Section 17.1, Seq is
an important class in Scala’s collection hierarchy. It’s a common superclass
of several classes describing different kinds of sequences: Lists, Arrays,
WrappedString, and several others.
For symmetry, Domain also has an apply method that builds a domain
string from a variable argument parameter of domain parts starting with the
top-level domain. As always, the apply method is optional.
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You can use the Domain extractor to get more detailed information out
of email strings. For instance, to search for an email address named "tom" in
some “.com” domain, you could write the following function:
def isTomInDotCom(s: String): Boolean = s match {
case EMail("tom", Domain("com", _*)) => true
case _ => false
}
This gives the expected results:
scala> isTomInDotCom("tom@sun.com")
res3: Boolean = true
scala> isTomInDotCom("peter@sun.com")
res4: Boolean = false
scala> isTomInDotCom("tom@acm.org")
res5: Boolean = false
It’s also possible to return some fixed elements from an unapplySeq together
with the variable part. This is expressed by returning all elements in a tuple,
where the variable part comes last, as usual. As an example, Listing 26.5
shows a new extractor for emails where the domain part is already expanded
into a sequence:
object ExpandedEMail {
def unapplySeq(email: String)
: Option[(String, Seq[String])] = {
val parts = email split "@"
if (parts.length == 2)
Some(parts(0), parts(1).split("\\.").reverse)
else
None
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}
}
Listing 26.5 · The ExpandedEMail extractor object.
The unapplySeq method in ExpandedEMail returns an optional value of a
pair (a Tuple2). The first element of the pair is the user part. The second
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element is a sequence of names representing the domain. You can match on
this as usual:
scala> val s = "tom@support.epfl.ch"
s: String = tom@support.epfl.ch
scala> val ExpandedEMail(name, topdom, subdoms @ _*) = s
name: String = tom
topdom: String = ch
subdoms: Seq[String] = WrappedArray(epfl, support)
26.5
Extractors and sequence patterns
You saw in Section 15.2 that you can access the elements of a list or an array
using sequence patterns such as:
List()
List(x, y, _*)
Array(x, 0, 0, _)
In fact, these sequence patterns are all implemented using extractors in the
standard Scala library. For instance, patterns of the form List(...) are
possible because the scala.List companion object is an extractor that defines an unapplySeq method. Listing 26.6 shows the relevant definitions:
package scala
object List {
def apply[T](elems: T*) = elems.toList
def unapplySeq[T](x: List[T]): Option[Seq[T]] = Some(x)
...
}
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Listing 26.6 · An extractor that defines an unapplySeq method.
The List object contains an apply method that takes a variable number of
arguments. That’s what lets you write expressions such as:
List()
List(1, 2, 3)
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It also contains an unapplySeq method that returns all elements of the list
as a sequence. That’s what supports List(...) patterns. Very similar definitions exist in the object scala.Array. These support analogous injections
and extractions for arrays.
26.6
Extractors versus case classes
Even though they are very useful, case classes have one shortcoming: they
expose the concrete representation of data. This means that the name of the
class in a constructor pattern corresponds to the concrete representation type
of the selector object. If a match against:
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case C(...)
succeeds, you know that the selector expression is an instance of class C.
Extractors break this link between data representations and patterns. You
have seen in the examples in this section that they enable patterns that have
nothing to do with the data type of the object that’s selected on. This property
is called representation independence. In open systems of large size, representation independence is very important because it allows you to change an
implementation type used in a set of components without affecting clients of
these components.
If your component had defined and exported a set of case classes, you’d
be stuck with them because client code could already contain pattern matches
against these case classes. Renaming some case classes or changing the class
hierarchy would affect client code. Extractors do not share this problem, because they represent a layer of indirection between a data representation and
the way it is viewed by clients. You could still change a concrete representation of a type, as long as you update all your extractors with it.
Representation independence is an important advantage of extractors
over case classes. On the other hand, case classes also have some advantages
of their own over extractors. First, they are much easier to set up and to define, and they require less code. Second, they usually lead to more efficient
pattern matches than extractors, because the Scala compiler can optimize
patterns over case classes much better than patterns over extractors. This is
because the mechanisms of case classes are fixed, whereas an unapply or
unapplySeq method in an extractor could do almost anything. Third, if your
case classes inherit from a sealed base class, the Scala compiler will check
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your pattern matches for exhaustiveness and will complain if some combination of possible values is not covered by a pattern. No such exhaustiveness
checks are available for extractors.
So which of the two methods should you prefer for your pattern matches?
It depends. If you write code for a closed application, case classes are usually preferable because of their advantages in conciseness, speed and static
checking. If you decide to change your class hierarchy later, the application
needs to be refactored, but this is usually not a problem. On the other hand, if
you need to expose a type to unknown clients, extractors might be preferable
because they maintain representation independence.
Fortunately, you need not decide right away. You could always start
with case classes and then, if the need arises, change to extractors. Because
patterns over extractors and patterns over case classes look exactly the same
in Scala, pattern matches in your clients will continue to work.
Of course, there are also situations where it’s clear from the start that
the structure of your patterns does not match the representation type of your
data. The email addresses discussed in this chapter were one such example.
In that case, extractors are the only possible choice.
26.7
Regular expressions
One particularly useful application area of extractors are regular expressions.
Like Java, Scala provides regular expressions through a library, but extractors
make it much nicer to interact with them.
Forming regular expressions
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Scala inherits its regular expression syntax from Java, which in turn inherits
most of the features of Perl. We assume you know that syntax already; if
not, there are many accessible tutorials, starting with the Javadoc documentation of class java.util.regex.Pattern. Here are just some examples
that should be enough as refreshers:
ab?
An ‘a’, possibly followed by a ‘b’.
\d+
A number consisting of one or more digits
represented by \d.
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[a-dA-D]\w*
A word starting with a letter between a and
d in lower or upper case, followed by a sequence of zero or more “word characters” denoted by \w. (A word character is a letter,
digit, or underscore.)
(-)?(\d+)(\.\d*)?
A number consisting of an optional minus
sign, followed by one or more digits, optionally followed by a period and zero or more
digits. The number contains three groups, i.e.,
the minus sign, the part before the decimal
point, and the fractional part including the
decimal point. Groups are enclosed in parentheses.
Scala’s regular expression class resides in package scala.util.matching.
scala> import scala.util.matching.Regex
A new regular expression value is created by passing a string to the Regex
constructor. For instance:
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scala> val Decimal = new Regex("(-)?(\\d+)(\\.\\d*)?")
Decimal: scala.util.matching.Regex = (-)?(\d+)(\.\d*)?
Note that, compared to the regular expression for decimal numbers given
previously, every backslash appears twice in the string above. This is because
in Java and Scala a single backslash is an escape character in a string literal,
not a regular character that shows up in the string. So instead of ‘\’ you need
to write ‘\\’ to get a single backslash in the string.
If a regular expression contains many backslashes this might be a bit
painful to write and to read. Scala’s raw strings provide an alternative. As
you saw in Section 5.2, a raw string is a sequence of characters between
triple quotes. The difference between a raw and a normal string is that all
characters in a raw string appear exactly as they are typed. This includes
backslashes, which are not treated as escape characters. So you could write
equivalently and somewhat more legibly:
scala> val Decimal = new Regex("""(-)?(\d+)(\.\d*)?""")
Decimal: scala.util.matching.Regex = (-)?(\d+)(\.\d*)?
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As you can see from the interpreter’s output, the generated result value for
Decimal is exactly the same as before.
Another, even shorter way to write a regular expression in Scala is this:
scala> val Decimal = """(-)?(\d+)(\.\d*)?""".r
Decimal: scala.util.matching.Regex = (-)?(\d+)(\.\d*)?
In other words, simply append a .r to a string to obtain a regular expression.
This is possible because there is a method named r in class StringOps,
which converts a string to a regular expression. The method is defined as
shown in Listing 26.7:
package scala.runtime
import scala.util.matching.Regex
class StringOps(self: String) ... {
...
def r = new Regex(self)
}
Listing 26.7 · How the r method is defined in StringOps.
Searching for regular expressions
You can search for occurrences of a regular expression in a string using several different operators:
regex findFirstIn str
Finds first occurrence of regular expression regex in string str, returning the result in an Option type.
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regex findAllIn str
Finds all occurrences of regular expression regex in string str, returning the results in an Iterator.
regex findPrefixOf str
Finds an occurrence of regular expression regex at the start of string
str, returning the result in an Option type.
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For instance, you could define the input sequence below and then search
decimal numbers in it:
scala> val Decimal = """(-)?(\d+)(\.\d*)?""".r
Decimal: scala.util.matching.Regex = (-)?(\d+)(\.\d*)?
scala> val input = "for -1.0 to 99 by 3"
input: String = for -1.0 to 99 by 3
scala> for (s <- Decimal findAllIn input)
println(s)
-1.0
99
3
scala> Decimal findFirstIn input
res7: Option[String] = Some(-1.0)
scala> Decimal findPrefixOf input
res8: Option[String] = None
Extracting with regular expressions
What’s more, every regular expression in Scala defines an extractor. The
extractor is used to identify substrings that are matched by the groups of the
regular expression. For instance, you could decompose a decimal number
string as follows:
scala> val Decimal(sign, integerpart, decimalpart) = "-1.23"
sign: String = integerpart: String = 1
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decimalpart: String = .23
In this example, the pattern, Decimal(...), is used in a val definition, as
described in Section 15.7. What happens here is that the Decimal regular expression value defines an unapplySeq method. That method matches every
string that corresponds to the regular expression syntax for decimal numbers.
If the string matches, the parts that correspond to the three groups in the regular expression (-)?(\d+)(\.\d*)? are returned as elements of the pattern
and are then matched by the three pattern variables sign, integerpart, and
decimalpart. If a group is missing, the element value is set to null, as can
be seen in the following example:
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scala> val Decimal(sign, integerpart, decimalpart) = "1.0"
sign: String = null
integerpart: String = 1
decimalpart: String = .0
It’s also possible to mix extractors with regular expression searches in a for
expression. For instance, the following expression decomposes all decimal
numbers it finds in the input string:
scala> for (Decimal(s, i, d) <- Decimal findAllIn input)
println("sign: " + s + ", integer: " +
i + ", decimal: " + d)
sign: -, integer: 1, decimal: .0
sign: null, integer: 99, decimal: null
sign: null, integer: 3, decimal: null
26.8
Conclusion
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In this chapter you saw how to generalize pattern matching with extractors.
Extractors let you define your own kinds of patterns, which need not correspond to the type of the expressions you select on. This gives you more
flexibility in the kinds of patterns you can use for matching. In effect it’s like
having different possible views on the same data. It also gives you a layer
between a type’s representation and the way clients view it. This lets you do
pattern matching while maintaining representation independence, a property
which is very useful in large software systems.
Extractors are one more element in your tool box that let you define
flexible library abstractions. They are used heavily in Scala’s libraries, for
instance, to enable convenient regular expression matching.
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636
Chapter 27
Annotations
Annotations are structured information added to program source code. Like
comments, they can be sprinkled throughout a program and attached to any
variable, method, expression, or other program element. Unlike comments,
they have structure, thus making them easier to machine process.
This chapter shows how to use annotations in Scala. It shows their general syntax and how to use several standard annotations.
This chapter does not show how to write new annotation processing
tools, because it is beyond the scope of this book. Chapter 31 shows one
technique, but not the only one. Instead, this chapter focuses on how to use
annotations, because it is more common to use annotations than to define
new annotation processors.
27.1
Why have annotations?
There are many things you can do with a program other than compiling and
running it. Some examples are:
1. Automatic generation of documentation as with Scaladoc.
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2. Pretty printing code so that it matches your preferred style.
3. Checking code for common errors such as opening a file but, on some
control paths, never closing it.
4. Experimental type checking, for example to manage side effects or
ensure ownership properties.
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Section 27.2
Chapter 27 · Annotations
Such tools are called meta-programming tools, because they are programs that take other programs as input. Annotations support these tools by
letting the programmer sprinkle directives to the tool throughout their source
code. Such directives let the tools be more effective than if they could have
no user input. For example, annotations can improve the previously listed
tools as follows:
1. A documentation generator could be instructed to document certain
methods as deprecated.
2. A pretty printer could be instructed to skip over parts of the program
that have been carefully hand formatted.
3. A checker for non-closed files could be instructed to ignore a particular
file that has been manually verified to be closed.
4. A side-effects checker could be instructed to verify that a specified
method has no side effects.
In all of these cases, it would in theory be possible for the programming
language to provide ways to insert the extra information. In fact, most of
these are directly supported in some language or another. However, there are
too many such tools for one language to directly support them all. Further,
all of this information is completely ignored by the compiler, which after all
just wants to make the code run.
Scala’s philosophy in cases like this is to include the minimum, orthogonal support in the core language such that a wide variety of metaprogramming tools can be written. In this case, that minimum support is
a system of annotations. The compiler understands just one feature, annotations, but it doesn’t attach any meaning to individual annotations. Each
meta-programming tool can then define and use its own specific annotations.
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27.2
Syntax of annotations
A typical use of an annotation looks like this:
@deprecated def bigMistake() = //...
The annotation is the @deprecated part, and it applies to the entirety of
the bigMistake method (not shown—it’s too embarrassing). In this case,
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the method is being marked as something the author of bigMistake wishes
you not to use. Maybe bigMistake will be removed entirely from a future
version of the code.
In the previous example, a method is annotated as @deprecated. Annotations are allowed in other places too. Annotations are allowed on any kind
of declaration or definition, including vals, vars, defs, classes, objects,
traits, and types. The annotation applies to the entirety of the declaration
or definition that follows it:
@deprecated class QuickAndDirty {
//...
}
Annotations can also be applied to an expression, as with the @unchecked
annotation for pattern matching (see Chapter 15). To do so, place a colon (:)
after the expression and then write the annotation. Syntactically, it looks like
the annotation is being used as a type:
(e: @unchecked) match {
// non-exhaustive cases...
}
Finally, annotations can be placed on types. Annotated types are described
later in this chapter.
So far the annotations shown have been simply an at sign followed by
an annotation class. Such simple annotations are common and useful, but
annotations have a richer general form:
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@annot(exp1 , exp2 , ...)
The annot specifies the class of annotation. All annotations must include that
much. The exp parts are arguments to the annotation. For annotations like
@deprecated that do not need any arguments, you would normally leave
off the parentheses, but you can write @deprecated() if you like. For annotations that do have arguments, place the arguments in parentheses, for
example, @serial(1234).
The precise form of the arguments you may give to an annotation depends on the particular annotation class. Most annotation processors only
let you supply immediate constants such as 123 or "hello". The compiler
itself supports arbitrary expressions, however, so long as they type check.
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Some annotation classes can make use of this, for example, to let you refer
to other variables that are in scope:
@cool val normal = "Hello"
@coolerThan(normal) val fonzy = "Heeyyy"
Internally, Scala represents an annotation as just a constructor call of an annotation class—replace the ‘@’ by ‘new’ and you have a valid instance creation expression. This means that named and default annotation arguments
are supported naturally, because Scala already has named and default arguments for method and constructor calls. One slightly tricky bit concerns
annotations that conceptually take other annotations as arguments, which are
required by some frameworks. You cannot write an annotation directly as an
argument to an annotation, because annotations are not valid expressions. In
such cases you must use ‘new’ instead of ‘@’, as illustrated here:
scala> import annotation._
import annotation._
scala> class strategy(arg: Annotation) extends Annotation
defined class strategy
scala> class delayed extends Annotation
defined class delayed
scala> @strategy(@delayed) def f() = {}
:1: error: illegal start of simple expression
@strategy(@delayed) def f() = {}
ˆ
scala> @strategy(new delayed) def f() = {}
f: ()Unit
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27.3
Standard annotations
Scala includes several standard annotations. They are for features that are
used widely enough to merit putting in the language specification, but that
are not fundamental enough to merit their own syntax. Over time, there
should be a trickle of new annotations that are added to the standard in just
the same way.
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Deprecation
Sometimes you write a class or method that you later wish you had not. Once
it is available, though, code written by other people might call the method.
Thus, you cannot simply delete the method, because this would cause other
people’s code to stop compiling.
Deprecation lets you gracefully remove a method or class that turns out
to be a mistake. You mark the method or class as deprecated, and then anyone who calls that method or class will get a deprecation warning. They had
better heed this warning and update their code! The idea is that after a suitable amount of time has passed, you feel safe in assuming that all reasonable
clients will have stopped accessing the deprecated class or method and thus
that you can safely remove it.
You mark a method as deprecated simply by writing @deprecated before it. For example:
@deprecated def bigMistake() =
//...
Such an annotation will cause the Scala compiler to emit deprecation warnings whenever Scala code accesses the method.
If you supply a string as an argument to @deprecated, that string will
be emitted along with the error message. Use this message to explain to
developers what they should use instead of the deprecated method.
@deprecated("use newShinyMethod() instead")
def bigMistake() =
//...
Now any callers will get a message like this:
$ scalac -deprecation Deprecation2.scala
Deprecation2.scala:33: warning: method bigMistake in object
Deprecation2 is deprecated: use newShinyMethod() instead
bigMistake()
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ˆ
one warning found
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Volatile fields
Concurrent programming does not mix well with shared mutable state. For
this reason, the focus of Scala’s concurrency support is message passing and
a minimum of shared mutable state. See Chapter ?? for the details.
Nonetheless, sometimes programmers want to use mutable state in their
concurrent programs. The @volatile annotation helps in such cases. It
informs the compiler that the variable in question will be used by multiple
threads. Such variables are implemented so that reads and writes to the variable are slower, but accesses from multiple threads behave more predictably.
The @volatile keyword gives different guarantees on different platforms. On the Java platform, however, you get the same behavior as if you
wrote the field in Java code and marked it with the Java volatile modifier.
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Binary serialization
Many languages include a framework for binary serialization. A serialization framework helps you convert objects into a stream of bytes and vice
versa. This is useful if you want to save objects to disk or send them over
the network. XML can help with the same goals (see Chapter 28), but it has
different trade offs regarding speed, space usage, flexibility, and portability.
Scala does not have its own serialization framework. Instead, you should
use a framework from your underlying platform. What Scala does is provide
three annotations that are useful for a variety of frameworks. Also, the Scala
compiler for the Java platform interprets these annotations in the Java way
(see Chapter 31).
The first annotation indicates whether a class is serializable at all. Most
classes are serializable, but not all. A handle to a socket or GUI window, for
example, cannot be serialized. By default, a class is not considered serializable. You should add a @serializable annotation to any class you would
like to be serializable.
The second annotation helps deal with serializable classes changing as
time goes by. You can attach a serial number to the current version of a
class by adding an annotation like @SerialVersionUID(1234), where 1234
should be replaced by your serial number of choice. The framework should
store this number in the generated byte stream. When you later reload that
byte stream and try to convert it to an object, the framework can check that
the current version of the class has the same version number as the version
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in the byte stream. If you want to make a serialization-incompatible change
to your class, then you can change the version number. The framework will
then automatically refuse to load old instances of the class.
Finally, Scala provides a @transient annotation for fields that should
not be serialized at all. If you mark a field as @transient, then the framework should not save the field even when the surrounding object is serialized.
When the object is loaded, the field will be restored to the default value for
the type of the field annotated as @transient.
Automatic get and set methods
Scala code normally does not need explicit get and set methods for fields,
because Scala blends the syntax for field access and method invocation.
Some platform-specific frameworks do expect get and set methods, however. For that purpose, Scala provides the @scala.reflect.BeanProperty
annotation. If you add this annotation to a field, the compiler will automatically generate get and set methods for you. If you annotate a field named
crazy, the get method will be named getCrazy and the set method will be
named setCrazy.
The generated get and set methods are only available after a compilation pass completes. Thus, you cannot call these get and set methods from
code you compile at the same time as the annotated fields. This should not
be a problem in practice, because in Scala code you can access the fields
directly. This feature is intended to support frameworks that expect regular
get and set methods, and typically you do not compile the framework and
the code that uses it at the same time.
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Tailrec
You would typically add the @tailrec annotation to a method that needs to
be tail recursive, for instance because you expect that it would recurse very
deeply otherwise. To make sure that the Scala compiler does perform the
tail-recursion optimization described in Section 8.9 on this method, you can
add @tailrec in front of the method definition. If the optimization cannot
be performed, you will then get a warning together with an explanation of
the reasons.
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Unchecked
The @unchecked annotation is interpreted by the compiler during pattern
matches. It tells the compiler not to worry if the match expression seems to
leave out some cases. See Section 15.5 for details.
Native methods
The @native annotation informs the compiler that a method’s implementation is supplied by the runtime rather than in Scala code. The compiler will
toggle the appropriate flags in the output, and it will be up to the developer
to supply the implementation using a mechanism such as the Java Native
Interface (JNI).
When using the @native annotation, a method body must be supplied,
but it will not be emitted into the output. For example, here is how to declare
that method beginCountdown will be supplied by the runtime:
@native
def beginCountdown() = {}
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27.4
Conclusion
This chapter described the platform-independent aspects of annotations that
you will most commonly need to know about. First of all it covered the syntax of annotations, because using annotations is far more common than defining new ones. Second it showed how to use several annotations that are supported by the standard Scala compiler, including @deprecated, @volatile,
@serializable, @BeanProperty, @tailrec, and @unchecked.
Chapter 31 gives additional, Java-specific information on annotations.
It covers annotations only available when targeting Java, additional meanings of standard annotations when targeting Java, how to interoperate with
Java-based annotations, and how to use Java-based mechanisms to define and
process annotations in Scala.
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644
Chapter 28
Working with XML
This chapter introduces Scala’s support for XML. After discussing semistructured data in general, it shows the essential functionality in Scala for
manipulating XML: how to make nodes with XML literals, how to save and
load XML to files, and how to take apart XML nodes using query methods
and pattern matching. This chapter is just a brief introduction to what is
possible with XML, but it shows enough to get you started.
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28.1
Semi-structured data
XML is a form of semi-structured data. It is more structured than plain
strings, because it organizes the contents of the data into a tree. Plain XML
is less structured than the objects of a programming language, though, as it
admits free-form text between tags and it lacks a type system.1
Semi-structured data is very helpful any time you need to serialize program data for saving in a file or shipping across a network. Instead of converting structured data all the way down to bytes, you convert it to and from
semi-structured data. You then use pre-existing library routines to convert
between semi-structured data and binary data, saving your time for more
important problems.
There are many forms of semi-structured data, but XML is the most
widely used on the Internet. There are XML tools on most operating systems, and most programming languages have XML libraries available. Its
popularity is self-reinforcing. The more tools and libraries are developed
1 There are type systems for XML, such as XML Schemas, but they are beyond the scope
of this book.
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Section 28.2
Chapter 28 · Working with XML
in response to XML’s popularity, the more likely software engineers are to
choose XML as part of their formats. If you write software that communicates over the Internet, then sooner or later you will need to interact with
some service that speaks XML.
For all of these reasons, Scala includes special support for processing
XML. This chapter shows you Scala’s support for constructing XML, processing it with regular methods, and processing it with Scala’s pattern matching. In addition to these nuts and bolts, the chapter shows along the way
several common idioms for using XML in Scala.
28.2
XML overview
XML is built out of two basic elements, text and tags.2 Text is, as usual, any
sequence of characters. Tags, written like , consist of a less-than sign,
an alphanumeric label, and a greater than sign. Tags can be start or end tags.
An end tag looks just like a start tag except that it has a slash just before the
tag’s label, like this: .
Start and end tags must match each other, just like parentheses. Any start
tag must eventually be followed by an end tag with the same label. Thus the
following is illegal:
// Illegal XML
One , two , three zoo
Further, the contents of any two matching tags must itself be valid XML.
You cannot have two pairs of matching tags overlap each other:
// Also illegal
Three in the
You could, however, write it like this:
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Three
Since tags are required to match in this way, XML is structured as nested
elements. Each pair of matching start and end tags forms an element, and
elements may be nested within each other. In the above example, the entirety of Three is an element, and
Three
Second, start tags can have attributes attached to them. An attribute is a
name-value pair written with an equals sign in the middle. The attribute
name itself is plain, unstructured text, and the value is surrounded by either
double quotes ("") or single quotes (''). Attributes look like this:
{yearMade}
else xml.NodeSeq.Empty }
res2: scala.xml.Elem =
1955
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If the code inside the curly braces evaluates to either an XML node or a sequence of XML nodes, those nodes are inserted directly as is. In the above
example, if yearMade is less than 2000, it is wrapped in tags and
added to the element. Otherwise, nothing is added. Note in the above example that “nothing” as an XML node is denoted with xml.NodeSeq.Empty.
An expression inside a brace escape does not have to evaluate to an XML
node. It can evaluate to any Scala value. In such a case, the result is converted
to a string and inserted as a text node:
scala> {3 + 4}
res3: scala.xml.Elem = 7
Any <, >, and & characters in the text will be escaped if you print the node
back out:
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Chapter 28 · Working with XML
scala> {"potential security hole"}
res4: scala.xml.Elem = </a>potential security
hole<a>
To contrast, if you create XML with low-level string operations, you will run
into traps such as the following:
scala> "" + "potential security hole" + ""
res5: String = potential security hole
What happens here is that a user-supplied string has included XML tags of its
own, in this case and . This behavior can allow some nasty surprises
for the original programmer, because it allows the user to affect the resulting
XML tree outside of the space provided for the user inside the element.
You can prevent this entire class of problems by always constructing XML
using XML literals, not string appends.
28.4
Serialization
You have now seen enough of Scala’s XML support to write the first part of
a serializer: conversion from internal data structures to XML. All you need
for this are XML literals and their brace escapes.
As an example, suppose you are implementing a database to keep track
of your extensive collection of vintage Coca-Cola thermometers. You might
make the following internal class to hold entries in the catalog:
abstract class CCTherm {
val description: String
val yearMade: Int
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val dateObtained: String
val bookPrice: Int
// in US cents
val purchasePrice: Int
// in US cents
val condition: Int
// 1 to 10
override def toString = description
}
This is a straightforward, data-heavy class that holds various pieces of information such as when the thermometer was made, when you got it, and how
much you paid for it.
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Section 28.4
Chapter 28 · Working with XML
To convert instances of this class to XML, simply add a toXML method
that uses XML literals and brace escapes, like this:
abstract class CCTherm {
...
def toXML =
{description}
{yearMade}
{dateObtained}
{bookPrice}
{purchasePrice}
{condition}
}
Here is the method in action:
scala> val therm = new CCTherm {
val description = "hot dog #5"
val yearMade = 1952
val dateObtained = "March 14, 2006"
val bookPrice = 2199
val purchasePrice = 500
val condition = 9
}
therm: CCTherm = hot dog #5
scala> therm.toXML
res6: scala.xml.Elem =
hot dog #5
1952
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March 14, 2006
2199
500
9
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Section 28.5
Chapter 28 · Working with XML
Note
The “new CCTherm” expression in the previous example works even
though CCTherm is an abstract class, because this syntax actually
instantiates an anonymous subclass of CCTherm. Anonymous classes were
described in Section 20.5.
By the way, if you want to include a curly brace (‘{’ or ‘}’) as XML text,
as opposed to using them to escape to Scala code, simply write two curly
braces in a row:
scala> {{{{brace yourself!}}}}
res7: scala.xml.Elem = {{brace yourself!}}
28.5
Taking XML apart
Among the many methods available for the XML classes, there are three in
particular that you should be aware of. They allow you to take apart XML
without thinking too much about the precise way XML is represented in
Scala. These methods are based on the XPath language for processing XML.
As is common in Scala, you can write them directly in Scala code instead of
needing to invoke an external tool.
Extracting text. By calling the text method on any XML node you retrieve all of the text within that node, minus any element tags:
scala> Sounds hello \ "b"
res10: scala.xml.NodeSeq = NodeSeq(hello )
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Section 28.6
Chapter 28 · Working with XML
You can do a “deep search” and look through sub-sub-elements, etc., by
using \\ instead of the \ operator:
scala>
hello \ "c"
res11: scala.xml.NodeSeq = NodeSeq()
scala>
hello \\ "c"
res12: scala.xml.NodeSeq = NodeSeq(hello )
scala>
hello \ "a"
res13: scala.xml.NodeSeq = NodeSeq()
scala>
hello \\ "a"
res14: scala.xml.NodeSeq =
NodeSeq(hello )
Note
Scala uses \ and \\ instead of XPath’s / and //. The reason is that //
starts a comment in Scala! Thus, some other symbol has to be used, and
using the other kind of slashes works well.
Extracting attributes.
You can extract tag attributes using the same \ and
\\ methods. Simply put an at sign (@) before the attribute name:
scala> val joe =
hot dog #5
1952
March 14, 2006
2199
500
9
scala> fromXML(node)
res17: CCTherm = hot dog #5
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28.7
Loading and saving
There is one last part needed to write a data serializer: conversion between
XML and streams of bytes. This last part is the easiest, because there are
library routines that will do it all for you. You simply have to call the right
routine on the right data.
To convert XML to a string, all you need is toString. The presence of a
workable toString is why you can experiment with XML in the Scala shell.
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Section 28.7
Chapter 28 · Working with XML
However, it is better to use a library routine and convert all the way to bytes.
That way, the resulting XML can include a directive that specifies which
character encoding was used. If you encode the string to bytes yourself, then
the onus is on you to keep track of the character encoding.
To convert from XML to a file of bytes, you can use the XML.save command. You must specify a file name and a node to be saved:
scala.xml.XML.save("therm1.xml", node)
After running the above command, the resulting file therm1.xml looks like
the following:
hot dog #5
1952
March 14, 2006
2199
500
9
Loading is simpler than saving, because the file includes everything the
loader needs to know. Simply call XML.loadFile on a file name:
scala> val loadnode = xml.XML.loadFile("therm1.xml")
loadnode: scala.xml.Elem =
hot dog #5
1952
March 14, 2006
2199
500
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9
scala> fromXML(loadnode)
res14: CCTherm = hot dog #5
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654
Section 28.8
Chapter 28 · Working with XML
Those are the basic methods you need. There are many variations on
these loading and saving methods, including methods for reading and writing
to various kinds of readers, writers, input and output streams.
28.8
Pattern matching on XML
So far you have seen how to dissect XML using text and the XPath-like
methods, \ and \\. These are good when you know exactly what kind of
XML structure you are taking apart. Sometimes, though, there are a few
possible structures the XML could have. Maybe there are multiple kinds of
records within the data, for example because you have extended your thermometer collection to include clocks and sandwich plates. Maybe you simply want to skip over any white space between tags. Whatever the reason,
you can use the pattern matcher to sift through the possibilities.
An XML pattern looks just like an XML literal. The main difference is
that if you insert a {} escape, then the code inside the {} is not an expression
but a pattern. A pattern embedded in {} can use the full Scala pattern language, including binding new variables, performing type tests, and ignoring
content using the _ and _* patterns. Here is a simple example:
def proc(node: scala.xml.Node): String =
node match {
case {contents} => "It's an a: " + contents
case {contents} => "It's a b: " + contents
case _ => "It's something else."
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}
This function has a pattern match with three cases. The first case looks
for an element whose contents consist of a single sub-node. It binds
those contents to a variable named contents and then evaluates the code
to the right of the associated right arrow (=>). The second case does the
same thing but looks for a instead of an , and the third case matches
anything not matched by any other case. Here is the function in use:
scala> proc(apple)
res18: String = It's an a: apple
scala> proc(banana)
res19: String = It's a b: banana
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655
Section 28.8
Chapter 28 · Working with XML
scala> proc(cherry )
res20: String = It's something else.
Most likely this function is not exactly what you want, because it looks
precisely for contents consisting of a single sub-node within the or .
Thus it will fail to match in cases like the following:
scala> proc(a red apple)
res21: String = It's something else.
scala> proc()
res22: String = It's something else.
If you want the function to match in cases like these, you can match
against a sequence of nodes instead of a single one. The pattern for “any
sequence” of XML nodes is written ‘_*’. Visually, this sequence looks like
the wildcard pattern (_) followed by a regex-style Kleene star (*). Here is
the updated function that matches a sequence of sub-elements instead of a
single sub-element:
def proc(node: scala.xml.Node): String =
node match {
case {contents @ _*} => "It's an a: " + contents
case {contents @ _*} => "It's a b: " + contents
case _ => "It's something else."
}
Notice that the result of the _* is bound to the contents variable by using
the @ pattern described in Section 15.2. Here is the new version in action:
scala> proc(a red apple)
res23: String = It's an a: ArrayBuffer(a , red,
apple)
scala> proc()
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res24: String = It's an a: WrappedArray()
As a final tip, be aware that XML patterns work very nicely with for
expressions as a way to iterate through some parts of an XML tree while
ignoring other parts. For example, suppose you wish to skip over the white
space between records in the following XML structure:
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Section 28.8
Chapter 28 · Working with XML
val catalog =
hot dog #5
1952
March 14, 2006
2199
500
9
Sprite Boy
1964
April 28, 2003
1695
595
5
Visually, it looks like there are two nodes inside the element.
Actually, though, there are five. There is white space before, after, and between the two elements! If you do not consider this white space, you might
incorrectly process the thermometer records as follows:
catalog match {
case {therms @ _*} =>
for (therm <- therms)
println("processing: " +
(therm \ "description").text)
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}
processing:
processing: hot dog #5
processing:
processing: Sprite Boy
processing:
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Chapter 28 · Working with XML
658
Notice all of the lines that try to process white space as if it were a true
thermometer record. What you would really like to do is ignore the white
space and process only those sub-nodes that are inside a element.
You can describe this subset using the pattern {_*} ,
and you can restrict the for expression to iterating over items that match that
pattern:
catalog match {
case {therms @ _*} =>
for (therm @ {_*} <-
therms)
println("processing: " +
(therm \ "description").text)
}
processing: hot dog #5
processing: Sprite Boy
28.9
Conclusion
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This chapter has only scratched the surface of what you can do with XML.
There are many other extensions, libraries, and tools you could learn about,
some customized for Scala, some made for Java but usable in Scala, and
some language-neutral. What you should walk away from this chapter with
is how to use semi-structured data for interchange, and how to access semistructured data via Scala’s XML support.
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Chapter 29
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Modular Programming Using Objects
In Chapter 1, we claimed that one way Scala is a scalable language is that
you can use the same techniques to construct small as well as large programs. So far in this book we’ve focused primarily on programming in the
small: designing and implementing the smaller program pieces out of which
you can construct a larger program.1 The other side of the story is programming in the large: organizing and assembling the smaller pieces into larger
programs, applications, or systems. We touched on this subject when we discussed packages and access modifiers in Chapter 13. In short, packages and
access modifiers enable you to organize a large program using packages as
modules, where a module is a “smaller program piece” with a well defined
interface and a hidden implementation.
While the division of programs into packages is already quite helpful, it
is limited because it provides no way to abstract. You cannot reconfigure a
package two different ways within the same program, and you cannot inherit
between packages. A package always includes one precise list of contents,
and that list is fixed until you change the code.
In this chapter, we’ll discuss how you can use Scala’s object-oriented
features to make a program more modular. We’ll first show how a simple
singleton object can be used as a module. Then we’ll explain how you can
use traits and classes as abstractions over modules. These abstractions can
be reconfigured into multiple modules, even multiple times within the same
program. Finally, we’ll show a pragmatic technique for using traits to divide
a module across multiple files.
1 This terminology was introduced in DeRemer, et al., “Programming-in-the-large versus
programming-in-the-small.” [DeR75]
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Section 29.1
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29.1
Chapter 29 · Modular Programming Using Objects
660
The problem
As a program grows in size, it becomes increasingly important to organize
it in a modular way. First, being able to compile different modules that
make up the system separately helps different teams work independently. In
addition, being able to unplug one implementation of a module and plug in
another is useful, because it allows different configurations of a system to
be used in different contexts, such as unit testing on a developer’s desktop,
integration testing, staging, and deployment.
For example, you may have an application that uses a database and a
message service. As you write code, you may want to run unit tests on your
desktop that use mock versions of both the database and message service,
which simulate these services sufficiently for testing without needing to talk
across the network to a shared resource. During integration testing, you may
want to use a mock message service but a live developer database. During
staging and certainly during deployment, your organization will likely want
to use live versions of both the database and message service.
Any technique that aims to facilitate this kind of modularity needs to provide a few essentials. First, there should be a module construct that provides
a good separation of interface and implementation. Second, there should be
a way to replace one module with another that has the same interface without changing or recompiling the modules that depend on the replaced one.
Lastly, there should be a way to wire modules together. This wiring task can
by thought of as configuring the system.
One approach to solving this problem is dependency injection, a technique supported on the Java platform by frameworks such as Spring and
Guice, which are popular in the enterprise Java community.2 Spring, for example, essentially allows you to represent the interface of a module as a Java
interface and implementations of the module as Java classes. You can specify
dependencies between modules and “wire” an application together via external XML configuration files. Although you can use Spring with Scala and
thereby use Spring’s approach to achieving system-level modularity of your
Scala programs, with Scala you have some alternatives enabled by the language itself. In the remainder of this chapter, we’ll show how to use objects
as modules to achieve the desired “in the large” modularity without using an
external framework.
2 Fowler, “Inversion of control containers and the dependency injection pattern.”
[Fow04]
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Section 29.2
29.2
Chapter 29 · Modular Programming Using Objects
A recipe application
Imagine you are building an enterprise web application that will allow users
to manage recipes. You want to partition the software into layers, including
a domain layer and an application layer. In the domain layer, you’ll define
domain objects, which will capture business concepts and rules, as well as
encapsulate state that will be persisted to an external relational database.
In the application layer, you’ll provide an API organized in terms of the
services the application offers to clients (including the user interface layer).
The application layer will implement these services by coordinating tasks
and delegating the work to the objects of the domain layer.3
You want to be able to plug in real or mock versions of certain objects
in each of these layers, so that you can more easily write unit tests for your
application. To achieve this goal, you can treat the objects you want to mock
as modules. In Scala, there is no need for objects to be “small” things, no
need to use some other kind of construct for “big” things like modules. One
of the ways Scala is a scalable language is that the same constructs are used
for structures both small and large.
For example, since one of the “things” you want to mock in the domain
layer is the object that represents the relational database, you’ll make that one
of the modules. In the application layer, you’ll treat a “database browser”
object as a module. The database will hold all of the recipes that a person
has collected. The browser will help search and browse that database, for
example, to find every recipe that includes an ingredient you have on hand.
The first thing to do is to model foods and recipes. To keep things simple,
a food will just have a name, as shown in Listing 29.1. A recipe will have a
name, a list of ingredients, and some instructions, as shown in Listing 29.2.
package org.stairwaybook.recipe
abstract class Food(val name: String) {
override def toString = name
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}
Listing 29.1 · A simple Food entity class.
3 The
naming of these layers follows that of Evans, Domain-Driven Design. [Eva03]
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package org.stairwaybook.recipe
class Recipe(
val name: String,
val ingredients: List[Food],
val instructions: String
) {
override def toString = name
}
Listing 29.2 · Simple Recipe entity class.
The Food and Recipe classes shown in Listings 29.1 and 29.2 represent
entities that will be persisted in the database.4 Listing 29.3 shows some
singleton instances of these classes, which can be used when writing tests.
package org.stairwaybook.recipe
object Apple extends Food("Apple")
object Orange extends Food("Orange")
object Cream extends Food("Cream")
object Sugar extends Food("Sugar")
object FruitSalad extends Recipe(
"fruit salad",
List(Apple, Orange, Cream, Sugar),
"Stir it all together."
)
Listing 29.3 · Food and Recipe examples for use in tests.
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Scala uses objects for modules, so you can start modularizing your program by making two singleton objects to serve as mock implementations
of the database and browser modules during testing. Because it is a mock,
4 These entity classes are simplified to keep the example uncluttered with too much
real-world detail. But transforming these classes into entities that could be persisted with
Hibernate or the Java Persistence Architecture, , for example, would require only a few
modifications, such as adding a private Long id field and a no-arg constructor, placing
scala.reflect.BeanProperty annotations on the fields, specifying appropriate mappings
via annotations or a separate XML file, and so on.
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package org.stairwaybook.recipe
object SimpleDatabase {
def allFoods = List(Apple, Orange, Cream, Sugar)
def foodNamed(name: String): Option[Food] =
allFoods.find(_.name == name)
def allRecipes: List[Recipe] = List(FruitSalad)
}
object SimpleBrowser {
def recipesUsing(food: Food) =
SimpleDatabase.allRecipes.filter(recipe =>
recipe.ingredients.contains(food))
}
Listing 29.4 · Mock database and browser modules.
the database module is backed by a simple in-memory list. Implementations
of these objects are shown in Listing 29.4. You can use this database and
browser as follows:
scala> val apple = SimpleDatabase.foodNamed("Apple").get
apple: Food = Apple
scala> SimpleBrowser.recipesUsing(apple)
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res0: List[Recipe] = List(fruit salad)
To make things a little more interesting, suppose the database sorts foods
into categories. To implement this, you can add a FoodCategory class and
a list of all categories in the database, as shown in Listing 29.5. Notice in
this example that the private keyword, so useful for implementing classes,
is also useful for implementing modules. Items marked private are part
of the implementation of a module, and thus are particularly easy to change
without affecting other modules.
At this point, many more facilities could be added, but you get the idea.
Programs can be divided into singleton objects, which you can think of as
modules. This is not big news, but it becomes very useful when you consider
abstraction (which we’ll cover next).
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package org.stairwaybook.recipe
object SimpleDatabase {
def allFoods = List(Apple, Orange, Cream, Sugar)
def foodNamed(name: String): Option[Food] =
allFoods.find(_.name == name)
def allRecipes: List[Recipe] = List(FruitSalad)
case class FoodCategory(name: String, foods: List[Food])
private var categories = List(
FoodCategory("fruits", List(Apple, Orange)),
FoodCategory("misc", List(Cream, Sugar)))
def allCategories = categories
}
object SimpleBrowser {
def recipesUsing(food: Food) =
SimpleDatabase.allRecipes.filter(recipe =>
recipe.ingredients.contains(food))
def displayCategory(category: SimpleDatabase.FoodCategory) = {
println(category)
}
}
Listing 29.5 · Database and browser modules with categories added.
29.3
Abstraction
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Although the examples shown so far did manage to partition your application into separate database and browser modules, the design is not yet very
“modular.” The problem is that there is essentially a “hard link” from the
browser module to the database modules:
SimpleDatabase.allRecipes.filter(recipe => ...
Because the SimpleBrowser module mentions the SimpleDatabase module by name, you won’t be able to plug in a different implementation of the
database module without modifying and recompiling the browser module.
In addition, although there’s no hard link from the SimpleDatabase module
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abstract class Browser {
val database: Database
def recipesUsing(food: Food) =
database.allRecipes.filter(recipe =>
recipe.ingredients.contains(food))
def displayCategory(category: database.FoodCategory) = {
println(category)
}
}
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Listing 29.6 · A Browser class with an abstract database val.
to the SimpleBrowser module,5 there’s no clear way to enable the user interface layer, for example, to be configured to use different implementations
of the browser module.
When making these modules more pluggable, however, it is important to
avoid duplicating code, because much code can likely be shared by different
implementations of the same module. For example, suppose you want the
same code base to support multiple recipe databases, and you want to be
able to create a separate browser for each of these databases. You would like
to reuse the browser code for each of the instances, because the only thing
different about the browsers is which database they refer to. Except for the
database implementation, the rest of the code can be reused character for
character. How can the program be arranged to minimize repetitive code?
How can the code be made reconfigurable, so that you can configure it using
either database implementation?
The answer is a familiar one: If a module is an object, then a template
for a module is a class. Just like a class describes the common parts of all its
instances, a class can describe the parts of a module that are common to all
of its possible configurations.
The browser definition therefore becomes a class, instead of an object,
and the database to use is specified as an abstract member of the class, as
shown in Listing 29.6. The database also becomes a class, including as much
as possible that is common between all databases, and declaring the missing
5 This
is good, because each of these architectural layers should depend only on layers
below them.
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parts that a database must define. In this case, all database modules must
define methods for allFoods, allRecipes, and allCategories, but since
they can use an arbitrary definition, the methods must be left abstract in the
Database class. The foodNamed method, by contrast, can be defined in the
abstract Database class, as shown in Listing 29.7.
abstract class Database {
def allFoods: List[Food]
def allRecipes: List[Recipe]
def foodNamed(name: String) =
allFoods.find(f => f.name == name)
case class FoodCategory(name: String, foods: List[Food])
def allCategories: List[FoodCategory]
}
Listing 29.7 · A Database class with abstract methods.
The SimpleDatabase object must be updated to inherit from the abstract
Database class, as shown in Listing 29.8.
object SimpleDatabase extends Database {
def allFoods = List(Apple, Orange, Cream, Sugar)
def allRecipes: List[Recipe] = List(FruitSalad)
private var categories = List(
FoodCategory("fruits", List(Apple, Orange)),
FoodCategory("misc", List(Cream, Sugar)))
def allCategories = categories
}
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Listing 29.8 · The SimpleDatabase object as a Database subclass.
Then, a specific browser module is made by instantiating the Browser class
and specifying which database to use, as shown in Listing 29.9.
You can use these more pluggable modules the same as before:
scala> val apple = SimpleDatabase.foodNamed("Apple").get
apple: Food = Apple
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object SimpleBrowser extends Browser {
val database = SimpleDatabase
}
Listing 29.9 · The SimpleBrowser object as a Browser subclass.
scala> SimpleBrowser.recipesUsing(apple)
res1: List[Recipe] = List(fruit salad)
Now, however, you can create a second mock database, and use the same
browser class with it, as shown in Listing 29.10:
object StudentDatabase extends Database {
object FrozenFood extends Food("FrozenFood")
object HeatItUp extends Recipe(
"heat it up",
List(FrozenFood),
"Microwave the 'food' for 10 minutes.")
def allFoods = List(FrozenFood)
def allRecipes = List(HeatItUp)
def allCategories = List(
FoodCategory("edible", List(FrozenFood)))
}
object StudentBrowser extends Browser {
val database = StudentDatabase
}
Listing 29.10 · A student database and browser.
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29.4
Splitting modules into traits
Often a module is too large to fit comfortably into a single file. When that
happens, you can use traits to split a module into separate files. For example,
suppose you wanted to move categorization code out of the main Database
file and into its own. You could create a trait for the code as shown in (Listing 29.11).
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trait FoodCategories {
case class FoodCategory(name: String, foods: List[Food])
def allCategories: List[FoodCategory]
}
Listing 29.11 · A trait for food categories.
Now class Database can mix in the FoodCategories trait instead of defining FoodCategory and allCategories itself, as shown in Listing 29.12:
abstract class Database extends FoodCategories {
def allFoods: List[Food]
def allRecipes: List[Recipe]
def foodNamed(name: String) =
allFoods.find(f => f.name == name)
}
Listing 29.12 · A Database class that mixes in the FoodCategories trait.
You might try and divide SimpleDatabase into two traits, one for foods and
one for recipes. This would allow you to define SimpleDatabase as shown
in Listing 29.13:
object SimpleDatabase extends Database
with SimpleFoods with SimpleRecipes
Listing 29.13 · A SimpleDatabase object composed solely of mixins.
The SimpleFoods trait could look as shown in Listing 29.14:
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trait SimpleFoods {
object Pear extends Food("Pear")
def allFoods = List(Apple, Pear)
def allCategories = Nil
}
Listing 29.14 · A SimpleFoods trait.
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So far so good, but unfortunately, a problem arises if you try to define a
SimpleRecipes trait like this:
trait SimpleRecipes { // Does not compile
object FruitSalad extends Recipe(
"fruit salad",
List(Apple, Pear),
// Uh oh
"Mix it all together."
)
def allRecipes = List(FruitSalad)
}
The problem is that Pear is located in a different trait from the one that uses
it, so it is out of scope. The compiler has no idea that SimpleRecipes is
only ever mixed together with SimpleFoods.
However, there is a way you can tell this to the compiler. Scala provides
the self type for precisely this situation. Technically, a self type is an assumed
type for this whenever this is mentioned within the class. Pragmatically,
a self type specifies the requirements on any concrete class the trait is mixed
into. If you have a trait that is only ever used when mixed in with another
trait or traits, then you can specify that those other traits should be assumed.
In the present case, it is enough to specify a self type of SimpleFoods, as
shown in Listing 29.15:
trait SimpleRecipes {
this: SimpleFoods =>
object FruitSalad extends Recipe(
"fruit salad",
List(Apple, Pear),
// Now Pear is in scope
"Mix it all together."
)
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def allRecipes = List(FruitSalad)
}
Listing 29.15 · A SimpleRecipes trait with a self type.
Given the new self type, Pear is now available. Implicitly, the reference
to Pear is thought of as this.Pear. This is safe, because any concrete
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class that mixes in SimpleRecipes must also be a subtype of SimpleFoods,
which means that Pear will be a member. Abstract subclasses and traits do
not have to follow this restriction, but since they cannot be instantiated with
new, there is no risk that the this.Pear reference will fail.
29.5
Runtime linking
Scala modules can be linked together at runtime, and you can decide which
modules will link to which depending on runtime computations. For example, Listing 29.16 shows a small program that chooses a database at runtime
and then prints out all the apple recipes in it:
object GotApples {
def main(args: Array[String]) = {
val db: Database =
if(args(0) == "student")
StudentDatabase
else
SimpleDatabase
object browser extends Browser {
val database = db
}
val apple = SimpleDatabase.foodNamed("Apple").get
for(recipe <- browser.recipesUsing(apple))
println(recipe)
}
}
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Listing 29.16 · An app that dynamically selects a module implementation.
Now, if you use the simple database, you will find a recipe for fruit salad. If
you use the student database, you will find no recipes at all using apples:
$ scala GotApples simple
fruit salad
$ scala GotApples student
$
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Configuring with Scala code
You may wonder if you are not backsliding to the hard links problem
of the original examples in this chapter, because the GotApples object
shown in Listing 29.16 contains hard links to both StudentDatabase
and SimpleDatabase. The difference here is that the hard links are
localized in one file that can be replaced.
Every modular application needs some way to specify the actual module
implementations to use in a particular situation. This act of “configuring” the application will by definition involve the naming of concrete
module implementations. For example, in a Spring application, you
configure by naming implementations in an external XML file. In
Scala, you can configure via Scala code itself. One advantage to using
Scala source over XML for configuration is that the process of running
your configuration file through the Scala compiler should uncover any
misspellings in it prior to its actual use.
29.6
Tracking module instances
Despite using the same code, the different browser and database modules created in the previous section really are separate modules. This means that each
module has its own contents, including any nested classes. FoodCategory
in SimpleDatabase, for example, is a different class from FoodCategory
in StudentDatabase!
scala> val category = StudentDatabase.allCategories.head
category: StudentDatabase.FoodCategory =
FoodCategory(edible,List(FrozenFood))
scala> SimpleBrowser.displayCategory(category)
:21: error: type mismatch;
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found
: StudentDatabase.FoodCategory
required: SimpleBrowser.database.FoodCategory
SimpleBrowser.displayCategory(category)
ˆ
If instead you prefer all FoodCategorys to be the same, you can accomplish
this by moving the definition of FoodCategory outside of any class or trait.
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The choice is yours, but as it is written, each Database gets its own, unique
FoodCategory class.
Since the two FoodCategory classes shown in this example really are
different, the compiler is correct to complain. Sometimes, though, you may
encounter a case where two types are the same but the compiler can’t verify
it. You will see the compiler complaining that two types are not the same,
even though you as the programmer know they are.
In such cases you can often fix the problem using singleton types. For
example, in the GotApples program, the type checker does not know that db
and browser.database are the same. This will cause type errors if you try
to pass categories between the two objects:
object GotApples {
// same definitions...
for (category <- db.allCategories)
browser.displayCategory(category)
// ...
}
GotApples2.scala:14: error: type mismatch;
found
: db.FoodCategory
required: browser.database.FoodCategory
browser.displayCategory(category)
ˆ
one error found
To avoid this error, you need to inform the type checker that they are the same
object. You can do this by changing the definition of browser.database as
shown in Listing 29.17:
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object browser extends Browser {
val database: db.type = db
}
Listing 29.17 · Using a singleton type.
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This definition is the same as before except that database has the funnylooking type db.type. The “.type” on the end means that this is a singleton type. A singleton type is extremely specific and holds only one object;
in this case, whichever object is referred to by db. Usually such types are
too specific to be useful, which is why the compiler is reluctant to insert
them automatically. In this case, though, the singleton type allows the compiler to know that db and browser.database are the same object—enough
information to eliminate the type error.
29.7
Conclusion
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This chapter has shown how to use Scala’s objects as modules. In addition
to simple static modules, this approach gives you a variety of ways to create
abstract, reconfigurable modules. There are actually even more abstraction
techniques than shown, since anything that works on a class also works on a
class used to implement a module. As always, how much of this power you
use should be a matter of taste.
Modules are part of programming in the large, and thus are hard to experiment with. You need a large program before it really makes a difference.
Nonetheless, after reading this chapter you know which Scala features to
think about when you want to program in a modular style. Think about these
techniques when you write your own large programs and recognize these
coding patterns when you see them in other people’s code.
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Chapter 30
Object Equality
Comparing two values for equality is ubiquitous in programming. It is also
more tricky than it looks at first glance. This chapter looks at object equality
in detail and gives some recommendations to consider when you design your
own equality tests.
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30.1
Equality in Scala
As mentioned in Section 11.2, the definition of equality is different in Scala
and Java. Java has two equality comparisons: the == operator, which is the
natural equality for value types and object identity for reference types, and
the equals method, which is (user-defined) canonical equality for reference
types. This convention is problematic because the more natural symbol, ==,
does not always correspond to the natural notion of equality. When programming in Java, a common pitfall for beginners is to compare objects with ==
when they should be compared with equals. For instance, comparing two
strings x and y using “x == y” might yield false in Java, even if x and y
have exactly the same characters in the same order.
Scala also has an equality method signifying object identity, but it is
not used much. That kind of equality, written “x eq y”, is true if x and
y reference the same object. The == equality is reserved in Scala for the
“natural” equality of each type. For value types, == is value comparison, just
like in Java. For reference types, == is the same as equals in Scala. You can
redefine the behavior of == for new types by overriding the equals method,
which is always inherited from class Any. The inherited equals, which takes
effect unless overridden, is object identity, as is the case in Java. So equals
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Chapter 30 · Object Equality
(and with it, ==) is by default the same as eq, but you can change its behavior
by overriding the equals method in the classes you define. It is not possible
to override == directly, as it is defined as a final method in class Any. That is,
Scala treats == as if it were defined as follows in class Any:
final def == (that: Any): Boolean =
if (null eq this) {null eq that} else {this equals that}
30.2
Writing an equality method
How should the equals method be defined? It turns out that writing a correct equality method is surprisingly difficult in object-oriented languages. In
fact, after studying a large body of Java code, the authors of a 2007 paper
concluded that almost all implementations of equals methods are faulty.1
This is problematic because equality is at the basis of many other things.
For one, a faulty equality method for a type C might mean that you cannot
reliably put an object of type C in a collection.
For example, you might have two elements, elem1 and elem2, of type
C which are equal (i.e., “elem1 equals elem2” yields true). Nevertheless,
with commonly occurring faulty implementations of the equals method,
you could still see behavior like the following:
var hashSet: Set[C] = new collection.immutable.HashSet
hashSet += elem1
hashSet contains elem2
// returns false!
Here are four common pitfalls2 that can cause inconsistent behavior when
overriding equals:
1. Defining equals with the wrong signature.
2. Changing equals without also changing hashCode.
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3. Defining equals in terms of mutable fields.
4. Failing to define equals as an equivalence relation.
These four pitfalls are discussed in the remainder of this section.
1 Vaziri,
et al., “Declarative Object Identity Using Relation Types” [Vaz07]
but the third pitfall are described in the context of Java in the book, Effective Java
Second Edition, by Joshua Bloch. [Blo08]
2 All
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Pitfall #1: Defining equals with the wrong signature
Consider adding an equality method to the following class of simple points:
class Point(val x: Int, val y: Int) { ... }
A seemingly obvious but wrong way would be to define it like this:
// An utterly wrong definition of equals
def equals(other: Point): Boolean =
this.x == other.x && this.y == other.y
What’s wrong with this method? At first glance, it seems to work OK:
scala> val p1, p2 = new Point(1, 2)
p1: Point = Point@37d7d90f
p2: Point = Point@3beb846d
scala> val q = new Point(2, 3)
q: Point = Point@e0cf182
scala> p1 equals p2
res0: Boolean = true
scala> p1 equals q
res1: Boolean = false
However, trouble starts once you start putting points into a collection:
scala> import scala.collection.mutable
import scala.collection.mutable
scala> val coll = mutable.HashSet(p1)
coll: scala.collection.mutable.HashSet[Point] =
Set(Point@37d7d90f)
scala> coll contains p2
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res2: Boolean = false
How to explain that coll does not contain p2, even though p1 was added
to it, and p1 and p2 are equal objects? The reason becomes clear in the
following interaction, where the precise type of one of the compared points
is masked. Define p2a as an alias of p2, but with type Any instead of Point:
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scala> val p2a: Any = p2
p2a: Any = Point@3beb846d
Now, were you to repeat the first comparison, but with the alias p2a instead
of p2, you would get:
scala> p1 equals p2a
res3: Boolean = false
What went wrong? The version of equals given previously does not override the standard method equals because its type is different. Here is the
type of the equals method as it is defined in the root class Any:3
def equals(other: Any): Boolean
Because the equals method in Point takes a Point instead of an Any as an
argument, it does not override equals in Any. Instead, it is just an overloaded
alternative. Now, overloading in Scala and in Java is resolved by the static
type of the argument, not the run-time type. So as long as the static type
of the argument is Point, the equals method in Point is called. However,
once the static argument is of type Any, the equals method in Any is called
instead. This method has not been overridden, so it is still implemented by
comparing object identity.
That’s why the comparison “p1 equals p2a” yields false even though
points p1 and p2a have the same x and y values. That’s also why the
contains method in HashSet returned false. Since that method operates
on generic sets, it calls the generic equals method in Object instead of the
overloaded variant in Point. Here’s a better equals method:
// A better definition, but still not perfect
override def equals(other: Any) = other match {
case that: Point => this.x == that.x && this.y == that.y
case _ => false
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}
Now equals has the correct type. It takes a value of type Any as parameter
and it yields a Boolean result. The implementation of this method uses a
3 If
you write a lot of Java, you might expect the argument to this method to be type
Object instead of type Any. Don’t worry about it; it is the same equals method. The
compiler simply makes it appear to have type Any.
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pattern match. It first tests whether the other object is also of type Point.
If it is, it compares the coordinates of the two points and returns the result.
Otherwise the result is false.
A related pitfall is to define == with a wrong signature. Normally, if you
try to redefine == with the correct signature, which takes an argument of type
Any, the compiler will give you an error because you try to override a final
method of type Any.
Newcomers to Scala sometimes make two errors at once: They try to
override == and they give it the wrong signature. For instance:
def ==(other: Point): Boolean = // Don't do this!
In this case, the user-defined == method is treated as an overloaded variant
of the same-named method class Any and the program compiles. However,
the behavior of the program would be just as dubious as if you had defined
equals with the wrong signature.
Pitfall #2: Changing equals without also changing hashCode
We’ll continue to use the example from pitfall #1. If you repeat the comparison of p1 and p2a with the latest definition of Point, you will get true,
as expected. However, if you repeat the HashSet.contains test, you will
probably still get false:
scala> val p1, p2 = new Point(1, 2)
p1: Point = Point@122c1533
p2: Point = Point@c23d097
scala> collection.mutable.HashSet(p1) contains p2
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res4: Boolean = false
But this outcome is not 100% certain. You might also get true from the
experiment. If you do, you can try with some other points with coordinates 1 and 2. Eventually, you’ll get one that is not contained in the set.
What goes wrong here is that Point redefined equals without also redefining hashCode.
Note that the collection in the example here is a HashSet. This means
elements of the collection are put in “hash buckets” determined by their hash
code. The contains test first determines a hash bucket to look in and then
compares the given elements with all elements in that bucket. Now, the last
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version of class Point did redefine equals, but it did not redefine hashCode
at the same time. So hashCode is still what it was in its version in class
AnyRef: some transformation of the address of the allocated object.
The hash codes of p1 and p2 are almost certainly different, even though
the fields of both points are the same. Different hash codes mean, with high
probability, different hash buckets in the set. The contains test will look
for a matching element in the bucket which corresponds to p2’s hash code.
In most cases, point p1 will be in another bucket, so it will never be found.
p1 and p2 might also end up by chance in the same hash bucket. In that case
the test would return true. The problem is that the last implementation of
Point violated the contract on hashCode as defined for class Any:4
If two objects are equal according to the equals method, then
calling the hashCode method on each of the two objects must
produce the same integer result.
In fact, it’s well known in Java that hashCode and equals should always be
redefined together. Furthermore, hashCode may only depend on fields that
equals depends on. For the Point class, the following would be a suitable
definition of hashCode:
class Point(val x: Int, val y: Int) {
override def hashCode = (x, y).##
override def equals(other: Any) = other match {
case that: Point => this.x == that.x && this.y == that.y
case _ => false
}
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}
This is just one of many possible implementations of hashCode. Recall that
the ## method is a shorthand for computing hash codes that works for primitive values, reference types, and null. When invoked on a collection or a
tuple, it computes a mixed hash that is sensitive to the hash codes of all the elements in the collection. We’ll provide more guidance on writing hashCode
later in this chapter.
Adding hashCode fixes the problems of equality when defining classes
like Point; however, there are other trouble spots to watch out for.
4 The
text of Any’s hashCode contract is inspired by the Javadoc documentation of class
java.lang.Object.
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Pitfall #3: Defining equals in terms of mutable fields
Consider the following slight variation of class Point:
class Point(var x: Int, var y: Int) { // Problematic
override def hashCode = (x, y).##
override def equals(other: Any) = other match {
case that: Point => this.x == that.x && this.y == that.y
case _ => false
}
}
The only difference is that the fields x and y are now vars instead of vals.
The equals and hashCode methods are now defined in terms of these mutable fields, so their results change when the fields change. This can have
strange effects once you put points in collections:
scala> val p = new Point(1, 2)
p: Point = Point@5428bd62
scala> val coll = collection.mutable.HashSet(p)
coll: scala.collection.mutable.HashSet[Point] =
Set(Point@5428bd62)
scala> coll contains p
res5: Boolean = true
Now, if you change a field in point p, does the collection still contain the
point? We’ll try it:
scala> p.x += 1
scala> coll contains p
res7: Boolean = false
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This looks strange. Where did p go? More strangeness results if you check
whether the iterator of the set contains p:
scala> coll.iterator contains p
res8: Boolean = true
So here’s a set that does not contain p, yet p is among the elements of the
set! What happened is that after the change to the x field, the point p ended
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up in the wrong hash bucket of the set coll. That is, its original hash bucket
no longer corresponded to the new value of its hash code. In a manner of
speaking, point p “dropped out of sight” in the set coll even though it still
belonged to its elements.
The lesson to be drawn from this example is that when equals and
hashCode depend on mutable state, it causes problems for potential users.
If you put such objects into collections, you have to be careful to never modify the depended-on state, and this is tricky. If you need a comparison that
takes the current state of an object into account, you should usually name it
something else, not equals.
Considering the last definition of Point, it would have been preferable to omit a redefinition of hashCode and name the comparison method
equalContents or some other name different from equals. Point would
then have inherited the default implementation of equals and hashCode; p
would have stayed locatable in coll even after the modification to its x field.
Pitfall #4: Failing to define equals as an equivalence relation
The contract of the equals method in scala.Any specifies that equals must
implement an equivalence relation on non-null objects:5
• It is reflexive: For any non-null value x , the expression x.equals(x)
should return true.
• It is symmetric: For any non-null values x and y, x.equals(y) should
return true if and only if y.equals(x) returns true.
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• It is transitive: For any non-null values x, y, and z, if x.equals(y) returns true and y.equals(z) returns true, then x.equals(z) should
return true.
• It is consistent: For any non-null values x and y, multiple invocations
of x.equals(y) should consistently return true or consistently return false, provided no information used in equals comparisons on
the objects is modified.
• For any non-null value x, x.equals(null) should return false.
5 As with hashCode, Any’s equals method contract is based on java.lang.Object’s
equals method contract.
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The definition of equals developed for class Point up to now satisfies
the contract for equals. However, things become more complicated once
subclasses are considered. Say there is a subclass ColoredPoint of Point
that adds a field color of type Color. Assume Color is defined as an enumeration, as presented in Section 20.9:
object Color extends Enumeration {
val Red, Orange, Yellow, Green, Blue, Indigo, Violet = Value
}
ColoredPoint overrides equals to take the new color field into account:
class ColoredPoint(x: Int, y: Int, val color: Color.Value)
extends Point(x, y) { // Problem: equals not symmetric
override def equals(other: Any) = other match {
case that: ColoredPoint =>
this.color == that.color && super.equals(that)
case _ => false
}
}
This is what many programmers would likely write. Note that in this case,
class ColoredPoint need not override hashCode. Because the new definition of equals on ColoredPoint is stricter than the overridden definition in
Point (meaning it equates fewer pairs of objects), the contract for hashCode
stays valid. If two colored points are equal, they must have the same coordinates, so their hash codes are guaranteed to be equal as well.
Taking the class ColoredPoint by itself, its definition of equals looks
OK. However, the contract for equals is broken once points and colored
points are mixed. Consider:
scala> val p = new Point(1, 2)
p: Point = Point@5428bd62
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scala> val cp = new ColoredPoint(1, 2, Color.Red)
cp: ColoredPoint = ColoredPoint@5428bd62
scala> p equals cp
res9: Boolean = true
scala> cp equals p
res10: Boolean = false
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The comparison “p equals cp” invokes p’s equals method, which is defined in class Point. This method only takes into account the coordinates
of the two points. Consequently, the comparison yields true. On the other
hand, the comparison “cp equals p” invokes cp’s equals method, which is
defined in class ColoredPoint. This method returns false because p is not
a ColoredPoint. So the relation defined by equals is not symmetric.
The loss in symmetry can have unexpected consequences for collections.
Here’s an example:
scala> collection.mutable.HashSet[Point](p) contains cp
res11: Boolean = true
scala> collection.mutable.HashSet[Point](cp) contains p
res12: Boolean = false
Even though p and cp are equal, one contains test succeeds whereas the
other one fails:
How can you change the definition of equals so that it becomes symmetric? Essentially there are two ways. You can either make the relation
more general or more strict. Making it more general means that a pair of
two objects, x and y, is taken to be equal if either comparing x with y or
comparing y with x yields true. Here’s code that does this:
class ColoredPoint(x: Int, y: Int, val color: Color.Value)
extends Point(x, y) { // Problem: equals not transitive
override def equals(other: Any) = other match {
case that: ColoredPoint =>
(this.color == that.color) && super.equals(that)
case that: Point =>
that equals this
case _ =>
false
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}
}
The new definition of equals in ColoredPoint has one more case than the
old one: If the other object is a Point but not a ColoredPoint, the method
forwards to the equals method of Point. This has the desired effect of
making equals symmetric. Now, both “cp equals p” and “p equals cp”
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result in true. However, the contract for equals is still broken. The problem
is that the new relation is no longer transitive!
Here’s a sequence of statements that demonstrates this. Define a point
and two colored points of different colors, all at the same position:
scala> val redp = new ColoredPoint(1, 2, Color.Red)
redp: ColoredPoint = ColoredPoint@5428bd62
scala> val bluep = new ColoredPoint(1, 2, Color.Blue)
bluep: ColoredPoint = ColoredPoint@5428bd62
Taken individually, redp is equal to p and p is equal to bluep:
scala> redp == p
res13: Boolean = true
scala> p == bluep
res14: Boolean = true
However, comparing redp and bluep yields false:
scala> redp == bluep
res15: Boolean = false
Hence, the transitivity clause of the equals’s contract is violated.
Making the equals relation more general seems to lead to a dead end.
We’ll try to make it stricter instead. One way to make equals stricter is to
always treat objects of different classes as different. This could be achieved
by modifying the equals methods in classes Point and ColoredPoint. In
class Point, you could add an extra comparison that checks whether the
run-time class of the other Point is exactly the same as this Point’s class:
// A technically valid, but unsatisfying, equals method
class Point(val x: Int, val y: Int) {
override def hashCode = (x, y).##
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override def equals(other: Any) = other match {
case that: Point =>
this.x == that.x && this.y == that.y &&
this.getClass == that.getClass
case _ => false
}
}
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You can then revert class ColoredPoint’s implementation back to the version that previously had violated the symmetry requirement:6
class ColoredPoint(x: Int, y: Int, val color: Color.Value)
extends Point(x, y) {
override def equals(other: Any) = other match {
case that: ColoredPoint =>
(this.color == that.color) && super.equals(that)
case _ => false
}
}
Here, an instance of class Point is considered to be equal to some other
instance of the same class, only if the objects have the same coordinates
and they have the same run-time class, meaning getClass on either object
returns the same value. The new definitions satisfy symmetry and transitivity
because now every comparison between objects of different classes yields
false. So a colored point can never be equal to a point. This convention
looks reasonable, but one could argue that the new definition is too strict.
Consider the following slightly roundabout way to define a point at coordinates (1, 2):
scala> val pAnon = new Point(1, 1) { override val y = 2 }
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pAnon: Point = $anon$1@5428bd62
Is pAnon equal to p? The answer is no because the java.lang.Class objects associated with p and pAnon are different. For p it is Point, whereas
for pAnon it is an anonymous subclass of Point. But clearly, pAnon is just
another point at coordinates (1, 2). It does not seem reasonable to treat it
as being different from p.
So it seems we are stuck. Is there a sane way to redefine equality on
several levels of the class hierarchy while keeping its contract? In fact, there
is such a way, but it requires one more method to redefine together with
equals and hashCode. The idea is that as soon as a class redefines equals
(and hashCode), it should also explicitly state that objects of this class are
never equal to objects of some superclass that implement a different equality
6 Given the new implementation of equals in Point, this version of ColoredPoint no
longer violates the symmetry requirement.
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method. This is achieved by adding a method canEqual to every class that
redefines equals. Here’s the method’s signature:
def canEqual(other: Any): Boolean
The method should return true if the other object is an instance of the class
in which canEqual is (re)defined, false otherwise. It is called from equals
to make sure that the objects are comparable both ways. Listing 30.1 shows
a new (and final) implementation of class Point along these lines:
class Point(val x: Int, val y: Int) {
override def hashCode = (x, y).##
override def equals(other: Any) = other match {
case that: Point =>
(that canEqual this) &&
(this.x == that.x) && (this.y == that.y)
case _ =>
false
}
def canEqual(other: Any) = other.isInstanceOf[Point]
}
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Listing 30.1 · A superclass equals method that calls canEqual.
The equals method in this version of class Point contains the additional
requirement that the other object can equal this one, as determined by the
canEqual method. The implementation of canEqual in Point states that all
instances of Point can be equal.
Listing 30.2 shows the corresponding implementation of ColoredPoint.
It can be shown that the new definition of Point and ColoredPoint keeps
the contract of equals. Equality is symmetric and transitive. Comparing a
Point to a ColoredPoint always yields false. Indeed, for any point p and
colored point cp, “p equals cp” will return false because “cp canEqual p”
will return false. The reverse comparison, “cp equals p”, will also return
false because p is not a ColoredPoint, so the first pattern match in the
body of equals in ColoredPoint will fail.
On the other hand, instances of different subclasses of Point can be
equal, as long as none of the classes redefines the equality method. For inCover · Overview · Contents · Discuss · Suggest · Glossary · Index
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class ColoredPoint(x: Int, y: Int, val color: Color.Value)
extends Point(x, y) {
override def hashCode = (super.hashCode, color).##
override def equals(other: Any) = other match {
case that: ColoredPoint =>
(that canEqual this) &&
super.equals(that) && this.color == that.color
case _ =>
false
}
override def canEqual(other: Any) =
other.isInstanceOf[ColoredPoint]
}
Listing 30.2 · A subclass equals method that calls canEqual.
stance, with the new class definitions, the comparison of p and pAnon would
yield true. Here are some examples:
scala> val p = new Point(1, 2)
p: Point = Point@5428bd62
scala> val cp = new ColoredPoint(1, 2, Color.Indigo)
cp: ColoredPoint = ColoredPoint@e6230d8f
scala> val pAnon = new Point(1, 1) { override val y = 2 }
pAnon: Point = $anon$1@5428bd62
scala> val coll = List(p)
coll: List[Point] = List(Point@5428bd62)
scala> coll contains p
res16: Boolean = true
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scala> coll contains cp
res17: Boolean = false
scala> coll contains pAnon
res18: Boolean = true
These examples demonstrate that if a superclass equals implementation defines and calls canEqual, then programmers who implement subclasses can
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decide whether or not their subclasses may be equal to instances of the superclass. Because ColoredPoint overrides canEqual, for example, a colored
point may never be equal to a plain-old point. But because the anonymous
subclass referenced from pAnon does not override canEqual, its instance can
be equal to a Point instance.
One potential criticism of the canEqual approach is that it violates the
Liskov Substitution Principle. For example, the technique of implementing
equals by comparing run-time classes, which led to the inability to define
a subclass whose instances can equal instances of the superclass, has been
described as a violation of the LSP.7 The LSP states you should be able to
use (substitute) a subclass instance where a superclass instance is required.
In the previous example, however, “coll contains cp” returned false
even though cp’s x and y values matched those of the point in the collection. Thus, it may seem like a violation of the LSP because you can’t use a
ColoredPoint here where a Point is expected. We believe this is the wrong
interpretation, because the LSP doesn’t require that a subclass behaves identically to its superclass, just that it behaves in a way that fulfills the contract
of its superclass.
The problem with writing an equals method that compares run-time
classes is not that it violates the LSP, but that it doesn’t give you a way
to create a subclass whose instances can equal superclass instances. For
example, had we used the run-time class technique in the previous example,
“coll contains pAnon” would have returned false, and that’s not what
we wanted. By contrast, we really did want “coll contains cp” to return
false, because by overriding equals in ColoredPoint, we were basically
saying that an indigo-colored point at coordinates (1, 2) is not the same thing
as an uncolored point at (1, 2). Thus, in the previous example we were able
to pass two different Point subclass instances to the collection’s contains
method, and we got back two different answers, both correct.
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30.3
Defining equality for parameterized types
The equals methods in the previous examples all started with a pattern
match that tested whether the type of the operand conformed to the type
of the class containing the equals method. When classes are parameterized,
this scheme needs to be adapted a little bit.
7 Bloch,
Effective Java Second Edition, p. 39 [Blo08]
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As an example, consider binary trees. The class hierarchy shown in Listing 30.3 defines an abstract class Tree for a binary tree, with two alternative
implementations: an EmptyTree object and a Branch class representing nonempty trees. A non-empty tree is made up of some element elem, and a left
and right child tree. The type of its element is given by a type parameter T.
trait Tree[+T] {
def elem: T
def left: Tree[T]
def right: Tree[T]
}
object EmptyTree extends Tree[Nothing] {
def elem =
throw new NoSuchElementException("EmptyTree.elem")
def left =
throw new NoSuchElementException("EmptyTree.left")
def right =
throw new NoSuchElementException("EmptyTree.right")
}
class Branch[+T](
val elem: T,
val left: Tree[T],
val right: Tree[T]
) extends Tree[T]
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Listing 30.3 · Hierarchy for binary trees.
We’ll now add equals and hashCode methods to these classes. For class
Tree itself there’s nothing to do because we assume that these methods are
implemented separately for each implementation of the abstract class. For
object EmptyTree, there’s still nothing to do because the default implementations of equals and hashCode that EmptyTree inherits from AnyRef work
just fine. After all, an EmptyTree is only equal to itself, so equality should
be reference equality, which is what’s inherited from AnyRef.
But adding equals and hashCode to Branch requires some work. A
Branch value should only be equal to other Branch values, and only if the
two values have equal elem, left and right fields. It’s natural to apply
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the schema for equals that was developed in the previous sections of this
chapter. This would give you:
class Branch[T](
val elem: T,
val left: Tree[T],
val right: Tree[T]
) extends Tree[T] {
override def equals(other: Any) = other match {
case that: Branch[T] => this.elem == that.elem &&
this.left == that.left &&
this.right == that.right
case _ => false
}
}
Compiling this example, however, gives an indication that “unchecked”
warnings occurred. Compiling again with the -unchecked option reveals
the following problem:
$ fsc -unchecked Tree.scala
Tree.scala:14: warning: non variable type-argument T in type
pattern is unchecked since it is eliminated by erasure
case that: Branch[T] => this.elem == that.elem &&
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ˆ
As the warning says, there is a pattern match against a Branch[T] type,
yet the system can only check that the other reference is (some kind of)
Branch; it cannot check that the element type of the tree is T. You encountered in Chapter 19 the reason for this: Element types of parameterized types
are eliminated by the compiler’s erasure phase; they are not available to be
inspected at run-time.
So what can you do? Fortunately, it turns out that you need not necessarily check that two Branches have the same element types when comparing
them. It’s quite possible that two Branches with different element types are
equal, as long as their fields are the same. A simple example of this would
be the Branch that consists of a single Nil element and two empty subtrees.
It’s plausible to consider any two such Branches to be equal, no matter what
static types they have:
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scala> val b1 = new Branch[List[String]](Nil,
EmptyTree, EmptyTree)
b1: Branch[List[String]] = Branch@9d5fa4f
scala> val b2 = new Branch[List[Int]](Nil,
EmptyTree, EmptyTree)
b2: Branch[List[Int]] = Branch@56cdfc29
scala> b1 == b2
res19: Boolean = true
The positive result of the comparison above was obtained with the implementation of equals on Branch shown previously. This demonstrates that
the element type of the Branch was not checked—if it had been checked, the
result would have been false.
We can disagree on which of the two possible outcomes of the comparison would be more natural. In the end, this depends on the mental model of
how classes are represented. In a model where type-parameters are present
only at compile-time, it’s natural to consider the two Branch values b1 and
b2 to be equal. In an alternative model where a type parameter forms part of
an object’s value, it’s equally natural to consider them different. Since Scala
adopts the type erasure model, type parameters are not preserved at run time,
so that b1 and b2 are naturally considered to be equal.
There’s only a tiny change needed to formulate an equals method that
does not produce an unchecked warning. Instead of an element type T, use
a lower case letter, such as t:
case that: Branch[t] => this.elem == that.elem &&
this.left == that.left &&
this.right == that.right
Recall from Section 15.2 that a type parameter in a pattern starting with a
lower-case letter represents an unknown type. Now the pattern match:
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case that: Branch[t] =>
will succeed for Branch values of any type. The type parameter t represents
the unknown element type of the Branch. It can also be replaced by an
underscore, as in the following case, which is equivalent to the previous one:
case that: Branch[_] =>
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The only thing that remains is to define for class Branch the other two methods, hashCode and canEqual, which go with equals. Here’s a possible
implementation of hashCode:
override def hashCode: Int = (elem, left, right).##
This is only one of many possible implementations. As shown previously, the
principle is to take hashCode values of all fields and combine them. Here’s
an implementation of method canEqual in class Branch:
def canEqual(other: Any) = other match {
case that: Branch[_] => true
case _ => false
}
The implementation of the canEqual method used a typed pattern match. It
would also be possible to formulate it with isInstanceOf:
def canEqual(other: Any) = other.isInstanceOf[Branch[_]]
If you feel like nit-picking—and we encourage you to do so!—you might
wonder what the occurrence of the underscore in the type above signifies.
After all, Branch[_] is technically a type parameter of a method, not a type
pattern. So how is it possible to leave some parts of it undefined?
The answer to this question is discussed in the next chapter. Branch[_]
is shorthand for a so-called wildcard type, which is, roughly speaking, a type
with some unknown parts in it. So even though technically the underscore
stands for two different things in a pattern match and in a type parameter of a
method call, in essence, the meaning is the same: It lets you label something
that is unknown. The final version of Branch is shown in Listing 30.4.
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30.4
Recipes for equals and hashCode
In this section, we’ll provide step-by-step recipes for creating equals and
hashCode methods that should suffice for most situations. As an illustration,
we’ll use the methods of class Rational, shown in Listing 30.5.
To create this class, we removed the mathematical operator methods
from the version of class Rational shown in Listing 6.5 on page 151. We
also made a minor enhancement to toString, and modified the initializers
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class Branch[T](
val elem: T,
val left: Tree[T],
val right: Tree[T]
) extends Tree[T] {
override def equals(other: Any) = other match {
case that: Branch[_] => (that canEqual this) &&
this.elem == that.elem &&
this.left == that.left &&
this.right == that.right
case _ => false
}
def canEqual(other: Any) = other.isInstanceOf[Branch[_]]
override def hashCode: Int = (elem, left, right).##
}
Listing 30.4 · A parameterized type with equals and hashCode.
of numer and denom to normalize all fractions to have a positive denominator
1
(i.e., to transform −2
to −1
2 ).
Recipe for equals
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Here’s the recipe for overriding equals:
1. To override equals in a non-final class, create a canEqual method.
If the inherited definition of equals is from AnyRef (that is, equals
was not redefined higher up in the class hierarchy), the definition of
canEqual should be new; otherwise, it will override a previous definition of a method with the same name. The only exception to this
requirement is for final classes that redefine the equals method inherited from AnyRef. For them the subclass anomalies described in Section 30.2 cannot arise; consequently they need not define canEqual.
The type of object passed to canEqual should be Any:
def canEqual(other: Any): Boolean =
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2. The canEqual method should yield true if the argument object is
an instance of the current class (i.e., the class in which canEqual is
defined), and false otherwise:
other.isInstanceOf[Rational]
3. In the equals method, make sure you declare the type of the object
passed as an Any:
override def equals(other: Any): Boolean =
4. Write the body of the equals method as a single match expression.
The selector of the match should be the object passed to equals:
other match {
// ...
}
5. The match expression should have two cases. The first case should declare a typed pattern for the type of the class on which you’re defining
the equals method:
case that: Rational =>
6. In the body of this case, write an expression that logical-ands together the individual expressions that must be true for the objects to be
equal. If the equals method you are overriding is not that of AnyRef,
you will most likely want to include an invocation of the superclass’s
equals method:
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super.equals(that) &&
If you are defining equals for a class that first introduced canEqual,
you should invoke canEqual on the argument to the equality method,
passing this as the argument:
(that canEqual this) &&
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Overriding redefinitions of equals should also include the canEqual
invocation, unless they contain a call to super.equals. In the latter
case, the canEqual test will already be done by the superclass call.
Lastly, for each field relevant to equality, verify that the field in this
object is equal to the corresponding field in the passed object:
numer == that.numer &&
denom == that.denom
7. For the second case, use a wildcard pattern that yields false:
case _ => false
If you adhere to this recipe for equals, equality is guaranteed to be an equivalence relation, as is required by the equals contract.
Recipe for hashCode
For hashCode, you can usually achieve satisfactory results if you use the
following recipe, which is similar to a recipe recommended for Java classes
in Effective Java.8 Include in the calculation each field in your object that
is used to determine equality in the equals method (the “relevant” fields).
Make a tuple containing the values of all those fields. Then, invoke ## on
the resulting tuple.
For example, to implement the hash code for an object that has five relevant fields named a, b, c, d, and e, you would write:
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override def hashCode: Int = (a, b, c, d, e).##
If the equals method invokes super.equals(that) as part of its calculation, you should start your hashCode calculation with an invocation of
super.hashCode. For example, had Rational’s equals method invoked
super.equals(that), its hashCode would have been:
override def hashCode: Int = (super.hashCode, numer, denom).##
8 Bloch,
Effective Java Second Edition. [Blo08]
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class Rational(n: Int, d: Int) {
require(d != 0)
private val g = gcd(n.abs, d.abs)
val numer = (if (d < 0) -n else n) / g
val denom = d.abs / g
private def gcd(a: Int, b: Int): Int =
if (b == 0) a else gcd(b, a % b)
override def equals(other: Any): Boolean =
other match {
case that: Rational =>
(that canEqual this) &&
numer == that.numer &&
denom == that.denom
case _ => false
}
def canEqual(other: Any): Boolean =
other.isInstanceOf[Rational]
override def hashCode: Int = (numer, denom).##
override def toString =
if (denom == 1) numer.toString else numer + "/" + denom
}
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Listing 30.5 · Class Rational with equals and hashCode.
One thing to keep in mind as you write hashCode methods using this approach is that your hash code will only be as good as the hash codes you
build out of it, namely the hash codes you obtain by calling hashCode on
the relevant fields of your object. Sometimes you may need to do something
extra besides just calling hashCode on the field to get a useful hash code for
that field. For example, if one of your fields is a collection, you probably
want a hash code for that field that is based on all the elements contained in
the collection. If the field is a Vector, List, Set, Map, or tuple, you can
simply include it in the list of items you are hashing over, because equals
and hashCode are overridden in those classes to take into account the conCover · Overview · Contents · Discuss · Suggest · Glossary · Index
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tained elements. However the same is not true for Arrays, which do not
take elements into account when calculating a hash code. Thus for an array,
you should treat each element of the array like an individual field of your
object, calling ## on each element explicitly or passing the array to one of
the hashCode methods in singleton object java.util.Arrays.
Lastly, if you find that a particular hash code calculation is harming the
performance of your program, consider caching the hash code. If the object
is immutable, you can calculate the hash code when the object is created and
store it in a field. You can do this by simply overriding hashCode with a val
instead of a def, like this:
override val hashCode: Int = (numer, denom).##
This approach trades off memory for computation time, because each instance of the immutable class will have one more field to hold the cached
hash code value.
30.5
Conclusion
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In retrospect, defining a correct implementation of equals has been surprisingly subtle. You must be careful about the type signature; you must override
hashCode; you should avoid dependencies on mutable state; and you should
implement and use a canEqual method if your class is non-final.
Given how difficult it is to implement a correct equality method, you
might prefer to define your classes of comparable objects as case classes.
That way, the Scala compiler will add equals and hashCode methods with
the right properties automatically.
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Chapter 31
Combining Scala and Java
Scala code is often used in tandem with large Java programs and frameworks. Since Scala is highly compatible with Java, most of the time you can
combine the languages without worrying very much. For example, standard
frameworks, such as Swing, Servlets, and JUnit, are known to work just fine
with Scala. Nonetheless, from time to time, you will run into some issue
combining Java and Scala.
This chapter describes two aspects of combining Java and Scala. First, it
discusses how Scala is translated to Java, which is especially important if you
call Scala code from Java. Second, it discusses the use of Java annotations
in Scala, an important feature if you want to use Scala with an existing Java
framework.
31.1
Using Scala from Java
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Most of the time you can think of Scala at the source code level. However,
you will have a richer understanding of how the system works if you know
something about its translation. Further, if you call Scala code from Java,
you will need to know what Scala code looks like from a Java point of view.
General rules
Scala is implemented as a translation to standard Java bytecodes. As much as
possible, Scala features map directly onto the equivalent Java features. For
example, Scala classes, methods, strings, and exceptions are all compiled to
the same in Java bytecode as their Java counterparts.
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Section 31.1
Chapter 31 · Combining Scala and Java
To make this happen required an occasional hard choice in the design of
Scala. For example, it might have been nice to resolve overloaded methods
at run time, using run-time types, rather than at compile time. Such a design
would break with Java’s, however, making it much trickier to mesh Java
and Scala. In this case, Scala stays with Java’s overloading resolution, and
thus Scala methods and method calls can map directly to Java methods and
method calls.
Scala has its own design for other features. For example, traits have no
equivalent in Java. Similarly, while both Scala and Java have generic types,
the details of the two systems clash. For language features like these, Scala
code cannot be mapped directly to a Java construct, so it must be encoded
using some combination of the structures Java does have.
For these features that are mapped indirectly, the encoding is not fixed.
There is an ongoing effort to make the translations as simple as possible so,
by the time you read this, some details may be different than at the time of
writing. You can find out what translation your current Scala compiler uses
by examining the “.class” files with tools like javap.
Those are the general rules. Consider now some special cases.
Value types
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A value type like Int can be translated in two different ways to Java. Whenever possible, the compiler translates a Scala Int to a Java int to get better
performance. Sometimes this is not possible, though, because the compiler
is not sure whether it is translating an Int or some other data type. For example, a particular List[Any] might hold only Ints, but the compiler has no
way to be sure.
In such cases, where the compiler is unsure whether an object is a value
type or not, the compiler uses objects and relies on wrapper classes. For
example, wrapper classes such as java.lang.Integer allow a value type
to be wrapped inside a Java object and thereby manipulated by code that
needs objects.1
Singleton objects
Java has no exact equivalent to a singleton object, but it does have static
methods. The Scala translation of singleton objects uses a combination of
1 The
implementation of value types was discussed in detail in Section 11.2.
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static and instance methods. For every Scala singleton object, the compiler
will create a Java class for the object with a dollar sign added to the end.
For a singleton object named App, the compiler produces a Java class named
App$. This class has all the methods and fields of the Scala singleton object.
The Java class also has a single static field named MODULE$ to hold the one
instance of the class that is created at run time.
As a full example, suppose you compile the following singleton object:
object App {
def main(args: Array[String]) = {
println("Hello, world!")
}
}
Scala will generate a Java App$ class with the following fields and methods:
$ javap App$
public final class App$ extends java.lang.Object
implements scala.ScalaObject{
public static final App$ MODULE$;
public static {};
public App$();
public void main(java.lang.String[]);
public int $tag();
}
That’s the translation for the general case. An important special case is if you
have a “standalone” singleton object, one which does not come with a class
of the same name. For example, you might have a singleton object named
App, and not have any class named App. In that case, the compiler will create
a Java class named App that has a static forwarder method for each method
of the Scala singleton object:
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$ javap App
Compiled from "App.scala"
public final class App extends java.lang.Object{
public static final int $tag();
public static final void main(java.lang.String[]);
}
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To contrast, if you did have a class named App, Scala would create a corresponding Java App class to hold the members of the App class you defined.
In that case it would not add any forwarding methods for the same-named
singleton object, and Java code would have to access the singleton via the
MODULE$ field.
Traits as interfaces
Compiling any trait creates a Java interface of the same name. This interface
is usable as a Java type, and it lets you call methods on Scala objects through
variables of that type.
Implementing a trait in Java is another story. In the general case it is not
practical; however, one special case is important. If you make a Scala trait
that includes only abstract methods, then that trait will be translated directly
to a Java interface with no other code to worry about. Essentially this means
that you can write a Java interface in Scala syntax if you like.
31.2
Annotations
Scala’s general annotations system is discussed in Chapter 27. This section
discusses Java-specific aspects of annotations.
Additional effects from standard annotations
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Several annotations cause the compiler to emit extra information when targeting the Java platform. When the compiler sees such an annotation, it first
processes it according to the general Scala rules, and then it does something
extra for Java.
Deprecation For any method or class marked @deprecated, the compiler
will add Java’s own deprecation annotation to the emitted code. Because of
this, Java compilers can issue deprecation warnings when Java code accesses
deprecated Scala methods.
Volatile fields Likewise, any field marked @volatile in Scala is given the
Java volatile modifier in the emitted code. Thus, volatile fields in Scala
behave exactly according to Java’s semantics, and accesses to volatile fields
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are sequenced precisely according to the rules specified for volatile fields in
the Java memory model.
Serialization Scala’s three standard serialization annotations are all translated to Java equivalents. A @serializable class has Java’s Serializable
interface added to it. A @SerialVersionUID(1234L) annotation is converted to the following Java field definition:
// Java serial version marker
private final static long SerialVersionUID = 1234L
Any variable marked @transient is given the Java transient modifier.
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Exceptions thrown
Scala does not check that thrown exceptions are caught. That is, Scala has
no equivalent to Java’s throws declarations on methods. All Scala methods
are translated to Java methods that declare no thrown exceptions.2
The reason this feature is omitted from Scala is that the Java experience with it has not been purely positive. Because annotating methods with
throws clauses is a heavy burden, too many developers write code that swallows and drops exceptions, just to get the code to compile without adding all
those throws clauses. They may intend to improve the exception handling
later, but experience shows that all too often time-pressed programmers will
never come back and add proper exception handling. The twisted result is
that this well-intentioned feature often ends up making code less reliable.
A large amount of production Java code swallows and hides runtime exceptions, and the reason it does so is to satisfy the compiler.
Sometimes when interfacing to Java, however, you may need to write
Scala code that has Java-friendly annotations describing which exceptions
your methods may throw. For example, each method in an RMI remote interface is required to mention java.io.RemoteException in its throws
clause. Thus, if you wish to write an RMI remote interface as a Scala
trait with abstract methods, you would need to list RemoteException in
the throws clauses for those methods. To accomplish this, all you have to
do is mark your methods with @throws annotations. For example, the Scala
class shown in Listing 31.1 has a method marked as throwing IOException.
2 The
reason it all works is that the Java bytecode verifier does not check the declarations
anyway! The Java compiler checks, but not the verifier.
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import java.io._
class Reader(fname: String) {
private val in =
new BufferedReader(new FileReader(fname))
@throws(classOf[IOException])
def read() = in.read()
}
Listing 31.1 · A Scala method that declares a Java throws clause.
Here is how it looks from Java:
$ javap Reader
Compiled from "Reader.scala"
public class Reader extends java.lang.Object implements
scala.ScalaObject{
public Reader(java.lang.String);
public int read()
throws java.io.IOException;
public int $tag();
}
$
Note that the read method indicates with a Java throws clause that it may
throw an IOException.
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Java annotations
Existing annotations from Java frameworks can be used directly in Scala
code. Any Java framework will see the annotations you write just as if you
were writing in Java.
A wide variety of Java packages use annotations. As an example, consider JUnit 4. JUnit is a framework for writing and running automated tests.
The latest version, JUnit 4, uses annotations to indicate which parts of your
code are tests. The idea is that you write a lot of tests for your code, and
then you run those tests whenever you change the source code. That way, if
your changes add a new bug, one of the tests will fail and you will find out
immediately.
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Writing a test is easy. You simply write a method in a top-level class that
exercises your code, and you use an annotation to mark the method as a test.
It looks like this:
import org.junit.Test
import org.junit.Assert.assertEquals
class SetTest {
@Test
def testMultiAdd = {
val set = Set() + 1 + 2 + 3 + 1 + 2 + 3
assertEquals(3, set.size)
}
}
The testMultiAdd method is a test. This test adds multiple items to a set
and makes sure that each is added only once. The assertEquals method,
which comes as part of the JUnit API, checks that its two arguments are
equal. If they are different, then the test fails. In this case, the test verifies
that repeatedly adding the same numbers does not increase the size of a set.
The test is marked using the annotation org.junit.Test. Note that
this annotation has been imported, so it can be referred to as simply @Test
instead of the more cumbersome @org.junit.Test.
That’s all there is to it. The test can be run using any JUnit test runner.
Here it is being run with the command-line test runner:
$ scala -cp junit-4.3.1.jar:. org.junit.runner.JUnitCore SetTest
JUnit version 4.3.1
.
Time: 0.023
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OK (1 test)
Writing your own annotations
To make an annotation that is visible to Java reflection, you must use Java
notation and compile it with javac. For this use case, writing the annotation
in Scala does not seem helpful, so the standard compiler does not support
it. The reasoning is that the Scala support would inevitably fall short of the
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full possibilities of Java annotations, and further, Scala will probably one
day have its own reflection, in which case you would want to access Scala
annotations with Scala reflection.
Here is an example annotation:
import java.lang.annotation.*; // This is Java
@Retention(RetentionPolicy.RUNTIME)
@Target(ElementType.METHOD)
public @interface Ignore { }
After compiling the above with javac, you can use the annotation as follows:
object Tests {
@Ignore
def testData = List(0, 1, -1, 5, -5)
def test1 = {
assert(testData == (testData.head :: testData.tail))
}
def test2 = {
assert(testData.contains(testData.head))
}
}
In this example, test1 and test2 are supposed to be test methods, but
testData should be ignored even though its name starts with “test”.
To see when these annotations are present, you can use the Java reflection
APIs. Here is sample code to show how it works:
for {
method <- Tests.getClass.getMethods
if method.getName.startsWith("test")
if method.getAnnotation(classOf[Ignore]) == null
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} {
println("found a test method: " + method)
}
Here, the reflective methods getClass and getMethods are used to inspect
all the fields of the input object’s class. These are normal reflection methods.
The annotation-specific part is the use of method getAnnotation. Many
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reflection objects have a getAnnotation method for searching for annotations of a specific type. In this case, the code looks for an annotation of our
new Ignore type. Since this is a Java API, success is indicated by whether
the result is null or an actual annotation object.
Here is the code in action:
$ javac Ignore.java
$ scalac Tests.scala
$ scalac FindTests.scala
$ scala FindTests
found a test method: public void Tests$.test2()
found a test method: public void Tests$.test1()
As an aside, notice that the methods are in class Tests$ instead of class
Tests when viewed with Java reflection. As described at the beginning of
the chapter, the implementation of a Scala singleton object is placed in a
Java class with a dollar sign added to the end of its name. In this case, the
implementation of Tests is in the Java class Tests$.
Be aware that when you use Java annotations you have to work within
their limitations. For example, you can only use constants, not expressions,
in the arguments to annotations. You can support @serial(1234) but not
@serial(x * 2), because x * 2 is not a constant.
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31.3
Wildcard types
All Java types have a Scala equivalent. This is necessary so that Scala code
can access any legal Java class. Most of the time the translation is straightforward. Pattern in Java is Pattern in Scala, and Iterator in
Java is Iterator[Component] in Scala. For some cases, though, the Scala
types you have seen so far are not enough. What can be done with Java wildcard types such as Iterator or Iterator?
What can be done about raw types like Iterator, where the type parameter is omitted? For Java wildcard types and raw types, Scala uses an extra
kind of type also called a wildcard type.
Wildcard types are written using placeholder syntax, just like the shorthand function literals described in Section 8.5. In the short hand for function
literals, you can use an underscore (_) in place of an expression; for example,
(_ + 1) is the same as (x => x + 1). Wildcard types use the same idea,
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only for types instead of expressions. If you write Iterator[_], then the
underscore is replacing a type. Such a type represents an Iterator where
the element type is not known.
You can also insert upper and lower bounds when using this placeholder
syntax. Simply add the bound after the underscore, using the same <: syntax
used with type parameters (Section 19.8 and Section 19.5). For example, the
type Iterator[_ <: Component] is an iterator where the element type is
not known, but whatever type it is, it must be a subtype of Component.
That’s how you write a wildcard type, but how do you use it? In simple
cases, you can ignore the wildcard and call methods on the base type. For
example, suppose you had the following Java class:
// This is a Java class with wildcards
public class Wild {
public Collection contents() {
Collection stuff = new Vector();
stuff.add("a");
stuff.add("b");
stuff.add("see");
return stuff;
}
}
If you access this in Scala code you will see that it has a wildcard type:
scala> val contents = (new Wild).contents
contents: java.util.Collection[_] = [a, b, see]
If you want to find out how many elements are in this collection, you can
simply ignore the wildcard part and call the size method as normal:
scala> contents.size()
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res0: Int = 3
In more complicated cases, wildcard types can be more awkward. Since the
wildcard type has no name, there is no way to use it in two separate places.
For example, suppose you wanted to create a mutable Scala set and initialize
it with the elements of contents:
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import scala.collection.mutable
val iter = (new Wild).contents.iterator
val set = mutable.Set.empty[???]
// what type goes here?
while (iter.hasMore)
set += iter.next()
A problem occurs on the third line. There is no way to name the type of
elements in the Java collection, so you cannot write down a satisfactory type
for set. To work around this kind of problem, here are two tricks you should
consider:
1. When passing a wildcard type into a method, give a parameter to the
method for the placeholder. You now have a name for the type that
you can use as many times as you like.
2. Instead of returning wildcard type from a method, return an object that
has abstract members for each of the placeholder types. (See Chapter 20 for information on abstract members.)
Using these two tricks together, the previous code can be written as follows:
import scala.collection.mutable
import java.util.Collection
abstract class SetAndType {
type Elem
val set: mutable.Set[Elem]
}
def javaSet2ScalaSet[T](jset: Collection[T]): SetAndType = {
val sset = mutable.Set.empty[T]
// now T can be named!
val iter = jset.iterator
while (iter.hasNext)
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sset += iter.next()
return new SetAndType {
type Elem = T
val set = sset
}
}
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You can see why Scala code normally does not use wildcard types. To do
anything sophisticated with them, you tend to convert them to use abstract
members. So you may as well use abstract members to begin with.
31.4
Compiling Scala and Java together
Usually when you compile Scala code that depends on Java code, you first
build the Java code to class files. You then build the Scala code, putting the
Java code’s class files on the classpath. However, this approach doesn’t work
if the Java code has references back into the Scala code. In such a case, no
matter which order you compile the code, one side or the other will have
unsatisfied external references. These situations are not uncommon; all it
takes is a mostly Java project where you replace one Java source file with a
Scala source file.
To support such builds, Scala allows compiling against Java source code
as well as Java class files. All you have to do is put the Java source files
on the command line as if they were Scala files. The Scala compiler won’t
compile those Java files, but it will scan them to see what they contain. To
use this facility, you first compile the Scala code using Java source files, and
then compile the Java code using Scala class files.
Here is a typical sequence of commands:
$ scalac -d bin InventoryAnalysis.scala InventoryItem.java \
Inventory.java
$ javac -cp bin -d bin Inventory.java InventoryItem.java \
InventoryManagement.java
$ scala -cp bin InventoryManagement
Most expensive item = sprocket($4.99)
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31.5
Java 8 integration in Scala 2.12
Java 8 added a few improvements to the Java language and bytecodes that
Scala takes advantage of in its 2.12 release.3 By exploiting new features of
Java 8, the Scala 2.12 compiler can generate smaller class and jar files and
improve the binary compatibility of traits.
3 Scala
2.12 requires Java 8 so that it can take advantage of Java 8 features.
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Lambda expressions and “SAM” types
From the Scala programmer’s perspective, the most visible Java 8-related enhancement in Scala 2.12 is that Scala function literals can be used like Java 8
lambda expressions as a more concise form for anonymous class instance expressions. To pass behavior into a method prior to Java 8, Java programmers
often defined anonymous inner class instances, like this:
JButton button = new JButton(); // This is Java
button.addActionListener(
new ActionListener() {
public void actionPerformed(ActionEvent event) {
System.out.println("pressed!");
}
}
);
In this example, an anonymous instance of ActionListener is created
and passed to the addActionListener of a Swing JButton. When a user
clicks on the button, Swing will invoke the actionPerformed method on
this instance, which will print "pressed!".
In Java 8, a lambda expression can be used anywhere an instance of a
class or interface that contains just a single abstract method (SAM) is required. ActionListener is such an interface, because it contains a single
abstract method, actionPerformed. Thus a lambda expression can be used
to register an action listener on a Swing button. Here’s an example:
JButton button = new JButton(); // This is Java 8
button.addActionListener(
event -> System.out.println("pressed!")
);
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In Scala, you could also use an anonymous inner class instance in the
same situation, but you might prefer to use a function literal, like this:
val button = new JButton
button.addActionListener(
_ => println("pressed!")
)
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As you have already seen in Section 21.1, you could support such a coding style by defining an implicit conversion from the ActionEvent => Unit
function type to ActionListener.
Scala 2.12 enables a function literal to be used in this case even in the
absence of such an implicit conversion. As with Java 8, Scala 2.12 will allow
a function type to be used where an instance of a class or trait declaring a
single abstract method (SAM) is required. This will work with any SAM in
Scala 2.12. For example, you might define a trait, Increaser, with a single
abstract method, increase:
scala> trait Increaser {
def increase(i: Int): Int
}
defined trait Increaser
You could then define a method that takes an Increaser:
scala> def increaseOne(increaser: Increaser): Int =
increaser.increase(1)
increaseOne: (increaser: Increaser)Int
To invoke your new method, you could pass in an anonymous instance of
trait Increaser, like this:
scala> increaseOne(
new Increaser {
def increase(i: Int): Int = i + 7
}
)
res0: Int = 8
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In Scala 2.12, however, you could alternatively just use a function literal,
because Increaser is a SAM type:
scala> increaseOne(i => i + 7) // Scala 2.12
res1: Int = 8
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Using Java 8 Streams from Scala 2.12
Java’s Stream is a functional data structure that offers a map method taking a
java.util.function.IntUnaryOperator. From Scala you could invoke
Stream.map to increment each element of an Array, like this:
scala> import java.util.function.IntUnaryOperator
import java.util.function.IntUnaryOperator
scala> import java.util.Arrays
import java.util.Arrays
scala> val stream = Arrays.stream(Array(1, 2, 3))
stream: java.util.stream.IntStream = ...
scala> stream.map(
new IntUnaryOperator {
def applyAsInt(i: Int): Int = i + 1
}
).toArray
res3: Array[Int] = Array(2, 3, 4)
Because IntUnaryOperator is a SAM type, however, you could in Scala
2.12 invoke it more concisely with a function literal:
scala> val stream = Arrays.stream(Array(1, 2, 3))
stream: java.util.stream.IntStream = ...
scala> stream.map(i => i + 1).toArray // Scala 2.12
res4: Array[Int] = Array(2, 3, 4)
Note that only function literals will be adapted to SAM types, not arbitrary expressions that have a function type. For example, consider the
following val, f, which has type Int => Int:
scala> val f = (i: Int) => i + 1
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f: Int => Int = ...
Although f has the same type as the function literal passed to stream.map
previously, you can’t use f where an IntUnaryOperator is required:
scala> val stream = Arrays.stream(Array(1, 2, 3))
stream: java.util.stream.IntStream = ...
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scala> stream.map(f).toArray
:16: error: type mismatch;
found
: Int => Int
required: java.util.function.IntUnaryOperator
stream.map(f).toArray
ˆ
To use f, you can explicitly call it using a function literal, like this:
scala> stream.map(i => f(i)).toArray
res5: Array[Int] = Array(2, 3, 4)
Or, you could annotate f with IntUnaryOperator, the type expected by
Stream.map, when you define f:
scala> val f: IntUnaryOperator = i => i + 1
f: java.util.function.IntUnaryOperator = ...
scala> val stream = Arrays.stream(Array(1, 2, 3))
stream: java.util.stream.IntStream = ...
scala> stream.map(f).toArray
res6: Array[Int] = Array(2, 3, 4)
With Scala 2.12 and Java 8, you can also invoke methods compiled
with Scala from Java, passing Scala function types using Java lambda expressions. Although Scala function types are defined as traits that include
concrete methods, Scala 2.12 compiles traits to Java interfaces with default
methods, a new feature of Java 8. As a result, Scala function types appear to
Java as SAMs.
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31.6
Conclusion
Most of the time, you can ignore how Scala is implemented, and simply write
and run your code. But sometimes it is nice to “look under the hood,” so this
chapter has gone into three aspects of Scala’s implementation on the Java
platform: What the translation looks like, how Scala and Java annotations
work together, and how Scala’s wildcard types let you access Java wildcard
types. It also covered using Java’s concurrency primitives from Scala and
compiling combined Scala and Java projects. These topics are important
whenever you use Scala and Java together.
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Futures and Concurrency
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One consequence of the proliferation of multicore processors has been an
increased interest in concurrency. Java provides concurrency support built
around shared memory and locking. Although this support is sufficient, this
approach turns out to be quite difficult to get right in practice. Scala’s standard library offers an alternative that avoids these difficulties by focusing on
asynchronous transformations of immutable state: the Future.
Although Java also offers a Future, it is very different from Scala’s.
Both represent the result of an asynchronous computation, but Java’s Future
requires that you access the result via a blocking get method. Although you
can call isDone to find out if a Java Future has completed before calling
get, thereby avoiding any blocking, you must wait until the Java Future has
completed before proceeding with any computation that uses the result.
By contrast, you can specify transformations on a Scala Future whether
it has completed or not. Each transformation results in a new Future representing the asynchronous result of the original Future transformed by the
function. The thread that performs the computation is determined by an
implicitly provided execution context. This allows you to describe asynchronous computations as a series of transformations of immutable values,
with no need to reason about shared memory and locks.
32.1
Trouble in paradise
On the Java platform, each object is associated with a logical monitor, which
can be used to control multi-threaded access to data. To use this model,
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Chapter 32 · Futures and Concurrency
chronized” sections of the code that access, or control access to, the shared
data. The Java runtime employs a locking mechanism to ensure that only
one thread at a time enters synchronized sections guarded by the same lock,
thereby enabling you to orchestrate multi-threaded access to the shared data.
For compatibility’s sake, Scala provides access to Java’s concurrency
primitives. The wait, notify, and notifyAll methods can be called in
Scala, and they have the same meaning as in Java. Scala doesn’t technically
have a synchronized keyword, but it includes a predefined synchronized
method that can be called as follows:
var counter = 0
synchronized {
// One thread in here at a time
counter = counter + 1
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}
Unfortunately, programmers have found it very difficult to reliably build
robust multi-threaded applications using the shared data and locks model,
especially as applications grow in size and complexity. The problem is that
at each point in the program, you must reason about what data you are modifying or accessing that might be modified or accessed by other threads, and
what locks are being held. At each method call, you must reason about what
locks it will try to hold and convince yourself that it will not deadlock while
trying to obtain them. Compounding the problem, the locks you reason about
are not fixed at compile time, because the program is free to create new locks
at run time as it progresses.
Making things worse, testing is not reliable with multi-threaded code.
Since threads are non-deterministic, you might successfully test a program
one thousand times—and the program could still go wrong the first time it
runs on a customer’s machine. With shared data and locks, you must get the
program correct through reason alone.
Moreover, you can’t solve the problem by over-synchronizing either. It
can be just as problematic to synchronize everything as it is to synchronize
nothing. Although new lock operations may remove possibilities for race
conditions, they simultaneously add possibilities for deadlocks. A correct
lock-using program must have neither race conditions nor deadlocks, so you
cannot play it safe by overdoing it in either direction.
The java.util.concurrent library provides higher level abstractions
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threaded programming far less error prone than rolling your own abstractions
with Java’s low-level synchronization primitives. Nevertheless, the concurrent utilities are also based on the shared data and locks model, and as a
result, do not solve the fundamental difficulties of using that model.
32.2
Asynchronous execution and Trys
Although not a silver bullet, Scala’s Future offers one way to deal with
concurrency that can reduce, and often eliminate, the need to reason about
shared data and locks. When you invoke a Scala method, it performs a computation “while you wait” and returns a result. If that result is a Future, the
Future represents another computation to be performed asynchronously, often by a completely different thread. As a result, many operations on Future
require an implicit execution context that provides a strategy for executing
functions asynchronously. For example, if you try to create a future via the
Future.apply factory method without providing an implicit execution context, an instance of scala.concurrent.ExecutionContext, you’ll get a
compiler error:
scala> import scala.concurrent.Future
import scala.concurrent.Future
scala> val fut = Future { Thread.sleep(10000); 21 + 21 }
:11: error: Cannot find an implicit ExecutionContext.
You might pass an (implicit ec: ExecutionContext)
parameter to your method or import
scala.concurrent.ExecutionContext.Implicits.global.
val fut = Future { Thread.sleep(10000); 21 + 21 }
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ˆ
The error message gives you one way to solve the problem: importing
a global execution context provided by Scala itself. On the JVM, the global
execution context uses a thread pool.1 Once you bring an implicit execution
context into scope, you can create a future:
scala> import scala.concurrent.ExecutionContext.Implicits.global
import scala.concurrent.ExecutionContext.Implicits.global
1 On
Scala.js, the global execution context places tasks on the JavaScript event queue.
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scala> val fut = Future { Thread.sleep(10000); 21 + 21 }
fut: scala.concurrent.Future[Int] = ...
The future created in the previous example asynchronously executes the
block of code, using the global execution context, then completes with the
value 42. Once it starts execution, that thread will sleep for ten seconds.
Thus this future will take at least ten seconds to complete.
Two methods on Future allow you to poll: isCompleted and value.
When invoked on a future that has not yet completed, isCompleted will
return false and value will return None.
scala> fut.isCompleted
res0: Boolean = false
scala> fut.value
res1: Option[scala.util.Try[Int]] = None
Once the future completes (in this case, after at least ten seconds has gone
by), isCompleted will return true and value will return a Some:
scala> fut.isCompleted
res2: Boolean = true
scala> fut.value
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res3: Option[scala.util.Try[Int]] = Some(Success(42))
The option returned by value contains a Try. As shown in Figure 32.1,
a Try is either a Success, which contains a value of type T, or a Failure,
which contains an exception (an instance of java.lang.Throwable). The
purpose of Try is to provide for asynchronous computations what the try expression provides for synchronous computations: It allows you to deal with
the possibility that the computation will complete abruptly with an exception
rather than return a result.2
For synchronous computations you can use try/catch to ensure that a
thread that invokes a method catches and handles exceptions thrown by the
method. For asynchronous computations, however, the thread that initiates
the computation often moves on to other tasks. Later if that asynchronous
2 Note that the Java Future also has a way to deal with the potential of an exception being
thrown by the asynchronous computation: its get method will throw that exception wrapped
in an ExecutionException.
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scala.util
Try[+T]
«sealed abstract»
scala.util
scala.util
Success[T]
Failure[T]
«final case»
«final case»
Figure 32.1 · Class hierarchy for Try.
computation fails with an exception, the original thread is no longer able to
handle the exception in a catch clause. Thus when working with a Future
representing an asynchronous activity, you use Try to deal with the possibility that the activity fails to yield a value and instead completes abruptly
an exception. Here’s an example that shows what happens when an asynchronous activity fails:
scala> val fut = Future { Thread.sleep(10000); 21 / 0 }
fut: scala.concurrent.Future[Int] = ...
scala> fut.value
res4: Option[scala.util.Try[Int]] = None
Then, after ten seconds:
scala> fut.value
res5: Option[scala.util.Try[Int]] =
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Some(Failure(java.lang.ArithmeticException: / by zero))
32.3
Working with Futures
Scala’s Future allows you to specify transformations on Future results and
obtain a new future that represents the composition of the two asynchronous
computations: the original and the transformation.
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Transforming Futures with map
The most fundamental such operation is map. Instead of blocking then continuing with another computation, you can just map the next computation
onto the future. The result will be a new future that represents the original
asynchronously computed result transformed asynchronously by the function passed to map.
For example, the following future will complete after ten seconds:
scala> val fut = Future { Thread.sleep(10000); 21 + 21 }
fut: scala.concurrent.Future[Int] = ...
Mapping this future with a function that increments by one will yield another future. This new future will represent a computation consisting of the
original addition followed by the subsequent increment:
scala> val result = fut.map(x => x + 1)
result: scala.concurrent.Future[Int] = ...
scala> result.value
res5: Option[scala.util.Try[Int]] = None
Once the original future completes and the function has been applied to its
result, the future returned by map will complete:
scala> result.value
res6: Option[scala.util.Try[Int]] = Some(Success(43))
Note that the operations performed in this example—the future creation, the
21 + 21 sum calculation, and the 42 + 1 increment—may be performed by
three different threads.
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Transforming Futures with for expressions
Because Scala’s future also declares a flatMap method, you can transform
futures using a for expression. For example, consider the following two
futures that will, after ten seconds, produce 42 and 46:
scala> val fut1 = Future { Thread.sleep(10000); 21 + 21 }
fut1: scala.concurrent.Future[Int] = ...
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scala> val fut2 = Future { Thread.sleep(10000); 23 + 23 }
fut2: scala.concurrent.Future[Int] = ...
Given these two futures, you can obtain a new future representing the asynchronous sum of their results like this:
scala> for {
x <- fut1
y <- fut2
} yield x + y
res7: scala.concurrent.Future[Int] = ...
Once the original futures have completed, and the subsequent sum completes, you’ll be able to see the result:
scala> res7.value
res8: Option[scala.util.Try[Int]] = Some(Success(88))
Because for expressions serialize their transformations,3 if you don’t create
the futures before the for expression, they won’t run in parallel. For example, although the previous for expression requires around ten seconds to
complete, the following for expression requires at least twenty seconds:
scala> for {
x <- Future { Thread.sleep(10000); 21 + 21 }
y <- Future { Thread.sleep(10000); 23 + 23 }
} yield x + y
res9: scala.concurrent.Future[Int] = ...
scala> res9.value
res27: Option[scala.util.Try[Int]] = None
scala> // Will need at least 20 seconds to complete
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scala> res9.value
res28: Option[scala.util.Try[Int]] = Some(Success(88))
3 The for expression shown in this example will be rewritten as a call to fut1.flatMap
passing in a function that calls fut2.map: fut1.flatMap(x => fut2.map(y => x + y)).
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Creating the Future: Future.failed, Future.successful,
Future.fromTry, and Promises
Besides the apply method, used in earlier examples to create futures, the
Future companion object also includes three factory methods for creating
already-completed futures: successful, failed, and fromTry. These factory methods do not require an ExecutionContext.
The successful factory method creates a future that has already succeeded:
scala> Future.successful { 21 + 21 }
res2: scala.concurrent.Future[Int] = ...
The failed method creates a future that has already failed:
scala> Future.failed(new Exception("bummer!"))
res3: scala.concurrent.Future[Nothing] = ...
The fromTry method creates an already completed future from a Try:
scala> import scala.util.{Success,Failure}
import scala.util.{Success, Failure}
scala> Future.fromTry(Success { 21 + 21 })
res4: scala.concurrent.Future[Int] = ...
scala> Future.fromTry(Failure(new Exception("bummer!")))
res5: scala.concurrent.Future[Nothing] = ...
The most general way to create a future is to use a Promise. Given a
promise you can obtain a future that is controlled by the promise. The future
will complete when you complete the promise. Here’s an example:
scala> val pro = Promise[Int]
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pro: scala.concurrent.Promise[Int] = ...
scala> val fut = pro.future
fut: scala.concurrent.Future[Int] = ...
scala> fut.value
res8: Option[scala.util.Try[Int]] = None
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You can complete the promise with methods named success, failure, and
complete. These methods on Promise are similar to those described previously for constructing already completed futures. For example, the success
method will complete the future successfully:
scala> pro.success(42)
res9: pro.type = ...
scala> fut.value
res10: Option[scala.util.Try[Int]] = Some(Success(42))
The failure method takes an exception that will cause the future to fail
with that exception. The complete method takes a Try. A completeWith
method, which takes a future, also exists; the promise’s future will thereafter
mirror the completion status of the future you passed to completeWith.
Filtering: filter and collect
Scala’s future offers two methods, filter and collect, that allow you to
ensure a property holds true about a future value. The filter method validates the future result, leaving it the same if it is valid. Here’s an example
that ensures an Int is positive:
scala> val fut = Future { 42 }
fut: scala.concurrent.Future[Int] = ...
scala> val valid = fut.filter(res => res > 0)
valid: scala.concurrent.Future[Int] = ...
scala> valid.value
res0: Option[scala.util.Try[Int]] = Some(Success(42))
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If the future value is not valid, the future returned by filter will fail with a
NoSuchElementException:
scala> val invalid = fut.filter(res => res < 0)
invalid: scala.concurrent.Future[Int] = ...
scala> invalid.value
res1: Option[scala.util.Try[Int]] =
Some(Failure(java.util.NoSuchElementException:
Future.filter predicate is not satisfied))
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Because Future also offers a withFilter method, you can perform the
same operation with for expression filters:
scala> val valid = for (res <- fut if res > 0) yield res
valid: scala.concurrent.Future[Int] = ...
scala> valid.value
res2: Option[scala.util.Try[Int]] = Some(Success(42))
scala> val invalid = for (res <- fut if res < 0) yield res
invalid: scala.concurrent.Future[Int] = ...
scala> invalid.value
res3: Option[scala.util.Try[Int]] =
Some(Failure(java.util.NoSuchElementException:
Future.filter predicate is not satisfied))
Future’s collect method allows you to validate the future value and
transform it in one operation. If the partial function passed to collect is
defined at the future result, the future returned by collect will succeed with
that value transformed by the function:
scala> val valid =
fut collect { case res if res > 0 => res + 46 }
valid: scala.concurrent.Future[Int] = ...
scala> valid.value
res17: Option[scala.util.Try[Int]] = Some(Success(88))
Otherwise, the future will fail with NoSuchElementException:
scala> val invalid =
fut collect { case res if res < 0 => res + 46 }
invalid: scala.concurrent.Future[Int] = ...
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scala> invalid.value
res18: Option[scala.util.Try[Int]] =
Some(Failure(java.util.NoSuchElementException:
Future.collect partial function is not defined at: 42))
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Dealing with failure: failed, fallBackTo, recover, and recoverWith
Scala’s future provides ways to work with futures that fail, including failed,
fallBackTo, recover, and recoverWith. The failed method will transform a failed future of any type into a successful Future[Throwable] that
holds onto the exception that caused the failure. Here’s an example:
scala> val failure = Future { 42 / 0 }
failure: scala.concurrent.Future[Int] = ...
scala> failure.value
res23: Option[scala.util.Try[Int]] =
Some(Failure(java.lang.ArithmeticException: / by zero))
scala> val expectedFailure = failure.failed
expectedFailure: scala.concurrent.Future[Throwable] = ...
scala> expectedFailure.value
res25: Option[scala.util.Try[Throwable]] =
Some(Success(java.lang.ArithmeticException: / by zero))
If the future on which the failed method is called ultimately succeeds, the
future returned by failed will itself fail with a NoSuchElementException.
The failed method is appropriate, therefore, only when you expect that the
future will fail. Here’s an example:
scala> val success = Future { 42 / 1 }
success: scala.concurrent.Future[Int] = ...
scala> success.value
res21: Option[scala.util.Try[Int]] = Some(Success(42))
scala> val unexpectedSuccess = success.failed
unexpectedSuccess: scala.concurrent.Future[Throwable] = ...
scala> unexpectedSuccess.value
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res26: Option[scala.util.Try[Throwable]] =
Some(Failure(java.util.NoSuchElementException:
Future.failed not completed with a throwable.))
The fallbackTo method allows you to provide an alternate future to use in
case the future on which you invoke fallbackTo fails. Here’s an example
in which a failed future falls back to a successful future:
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scala> val fallback = failure.fallbackTo(success)
fallback: scala.concurrent.Future[Int] = ...
scala> fallback.value
res27: Option[scala.util.Try[Int]] = Some(Success(42))
If the original future on which fallbackTo is invoked fails, a failure of the
future passed to fallbackTo is essentially ignored. The future returned by
fallbackTo will fail with the initial exception. Here’s an example:
scala> val failedFallback = failure.fallbackTo(
Future { val res = 42; require(res < 0); res }
)
failedFallback: scala.concurrent.Future[Int] = ...
scala> failedFallback.value
res28: Option[scala.util.Try[Int]] =
Some(Failure(java.lang.ArithmeticException: / by zero))
The recover method allows you to transform a failed future into a successful one, allowing a successful future’s result to pass through unchanged.
For example, on a future that fails with ArithmeticException, you can use
the recover method to transform the failure into a success, like this:
scala> val recovered = failedFallback recover {
case ex: ArithmeticException => -1
}
recovered: scala.concurrent.Future[Int] = ...
scala> recovered.value
res32: Option[scala.util.Try[Int]] = Some(Success(-1))
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If the original future doesn’t fail, the future returned by recover will complete with the same value:
scala> val unrecovered = fallback recover {
case ex: ArithmeticException => -1
}
unrecovered: scala.concurrent.Future[Int] = ...
scala> unrecovered.value
res33: Option[scala.util.Try[Int]] = Some(Success(42))
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Similarly, if the partial function passed to recover isn’t defined at the exception with which the original future ultimately fails, that original failure
will pass through:
scala> val alsoUnrecovered = failedFallback recover {
case ex: IllegalArgumentException => -2
}
alsoUnrecovered: scala.concurrent.Future[Int] = ...
scala> alsoUnrecovered.value
res34: Option[scala.util.Try[Int]] =
Some(Failure(java.lang.ArithmeticException: / by zero))
The recoverWith method is similar to recover, except instead of recovering to a value like recover, the recoverWith method allows you to recover
to a future value. Here’s an example:
scala> val alsoRecovered = failedFallback recoverWith {
case ex: ArithmeticException => Future { 42 + 46 }
}
alsoRecovered: scala.concurrent.Future[Int] = ...
scala> alsoRecovered.value
res35: Option[scala.util.Try[Int]] = Some(Success(88))
As with recover, if either the original future doesn’t fail, or the partial function passed to recoverWith isn’t defined at the exception the original future
ultimately fails with, the original success (or failure) will pass through to the
future returned by recoverWith.
Mapping both possibilities: transform
Future’s transform method accepts two functions with which to transform
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a future: one to use in case of success and the other in case of failure:
scala> val first = success.transform(
res => res * -1,
ex => new Exception("see cause", ex)
)
first: scala.concurrent.Future[Int] = ...
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If the future succeeds, the first function is used:
scala> first.value
res42: Option[scala.util.Try[Int]] = Some(Success(-42))
If the future fails, the second function is used:
scala> val second = failure.transform(
res => res * -1,
ex => new Exception("see cause", ex)
)
second: scala.concurrent.Future[Int] = ...
scala> second.value
res43: Option[scala.util.Try[Int]] =
Some(Failure(java.lang.Exception: see cause))
Note that with the transform method shown in the previous examples, you
can’t change a successful future into a failed one, nor can you change a failed
future into a successful one. To make this kind of transformation easier,
Scala 2.12 introduced an alternate overloaded form of transform that takes
a function from Try to Try. Here are some examples:
scala> val firstCase = success.transform { // Scala 2.12
case Success(res) => Success(res * -1)
case Failure(ex) =>
Failure(new Exception("see cause", ex))
}
first: scala.concurrent.Future[Int] = ...
scala> firstCase.value
res6: Option[scala.util.Try[Int]] = Some(Success(-42))
scala> val secondCase = failure.transform {
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case Success(res) => Success(res * -1)
case Failure(ex) =>
Failure(new Exception("see cause", ex))
}
secondCase: scala.concurrent.Future[Int] = ...
scala> secondCase.value
res8: Option[scala.util.Try[Int]] =
Some(Failure(java.lang.Exception: see cause))
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Here’s an example of using the new transform method to transform a failure
into a success:
scala> val nonNegative = failure.transform { // Scala 2.12
case Success(res) => Success(res.abs + 1)
case Failure(_) => Success(0)
}
nonNegative: scala.concurrent.Future[Int] = ...
scala> nonNegative.value
res11: Option[scala.util.Try[Int]] = Some(Success(0))
Combining futures: zip, Future.fold, Future.reduce,
Future.sequence, and Future.traverse
Future and its companion object offer methods that allow you to combine
multiple futures. The zip method will transform two successful futures into
a future tuple of both values. Here’s an example:
scala> val zippedSuccess = success zip recovered
zippedSuccess: scala.concurrent.Future[(Int, Int)] = ...
scala> zippedSuccess.value
res46: Option[scala.util.Try[(Int, Int)]] =
Some(Success((42,-1)))
If either of the futures fail, however, the future returned by zip will also fail
with the same exception:
scala> val zippedFailure = success zip failure
zippedFailure: scala.concurrent.Future[(Int, Int)] = ...
scala> zippedFailure.value
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res48: Option[scala.util.Try[(Int, Int)]] =
Some(Failure(java.lang.ArithmeticException: / by zero))
If both futures fail, the failed future that results will contain the exception
stored in the initial future, the one on which zip was invoked.
Future’s companion object offers a fold method that allows you to accumulate a result across a TraversableOnce collection of futures, yielding a
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future result. If all futures in the collection succeed, the resulting future will
succeed with the accumulated result. If any future in the collection fails, the
resulting future will fail. If multiple futures fail, the result will fail with the
same exception with which the first future (earliest in the TraversableOnce
collection) fails. Here’s an example:
scala> val fortyTwo = Future { 21 + 21 }
fortyTwo: scala.concurrent.Future[Int] = ...
scala> val fortySix = Future { 23 + 23 }
fortySix: scala.concurrent.Future[Int] = ...
scala> val futureNums = List(fortyTwo, fortySix)
futureNums: List[scala.concurrent.Future[Int]] = ...
scala> val folded =
Future.fold(futureNums)(0) { (acc, num) =>
acc + num
}
folded: scala.concurrent.Future[Int] = ...
scala> folded.value
res53: Option[scala.util.Try[Int]] = Some(Success(88))
The Future.reduce method performs a fold without a zero, using the initial
future result as the start value. Here’s an example:
scala> val reduced =
Future.reduce(futureNums) { (acc, num) =>
acc + num
}
reduced: scala.concurrent.Future[Int] = ...
scala> reduced.value
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res54: Option[scala.util.Try[Int]] = Some(Success(88))
If you pass an empty collection to reduce, the resulting future will fail with
a NoSuchElementException.
The Future.sequence method transforms a TraversableOnce collection of futures into a future TraversableOnce of values. For instance, in the
following example, sequence is used to transform a List[Future[Int]]
to a Future[List[Int]]:
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scala> val futureList = Future.sequence(futureNums)
futureList: scala.concurrent.Future[List[Int]] = ...
scala> futureList.value
res55: Option[scala.util.Try[List[Int]]] =
Some(Success(List(42, 46)))
The Future.traverse method will change a TraversableOnce of any
element type into a TraversableOnce of futures and “sequence” that into
a future TraversableOnce of values. For example, here a List[Int] is
transformed into a Future[List[Int]] by Future.traverse:
scala> val traversed =
Future.traverse(List(1, 2, 3)) { i => Future(i) }
traversed: scala.concurrent.Future[List[Int]] = ...
scala> traversed.value
res58: Option[scala.util.Try[List[Int]]] =
Some(Success(List(1, 2, 3)))
Performing side-effects: foreach, onComplete, and andThen
Sometimes you may need to perform a side effect after a future completes.
Future provides several methods for this purpose. The most basic method is
foreach, which will perform a side effect if a future completes successfully.
For instance, in the following example a println is not executed in the case
of a failed future, just a successful future:
scala> failure.foreach(ex => println(ex))
scala> success.foreach(res => println(res))
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42
Since for without yield will rewrite to an invocation of foreach, you can
also accomplish the same effect using for expressions:
scala> for (res <- failure) println(res)
scala> for (res <- success) println(res)
42
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Future also offers two methods for registering “callback” functions. The
onComplete method will be executed whether the future ultimately succeeds
or fails. The function will be passed a Try—a Success holding the result if
the future succeeded, else a Failure holding the exception that caused the
future to fail. Here’s an example:
scala> import scala.util.{Success, Failure}
import scala.util.{Success, Failure}
scala> success onComplete {
case Success(res) => println(res)
case Failure(ex) => println(ex)
}
42
scala> failure onComplete {
case Success(res) => println(res)
case Failure(ex) => println(ex)
}
java.lang.ArithmeticException: / by zero
Future does not guarantee any order of execution for callback functions registered with onComplete. If you want to enforce an order for callback functions, you must use andThen instead. The andThen method returns a new
future that mirrors (succeeds or fails in the same way as) the original future
on which you invoke andThen, but it does not complete until the callback
function has been fully executed:
scala> val newFuture = success andThen {
case Success(res) => println(res)
case Failure(ex) => println(ex)
}
42
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newFuture: scala.concurrent.Future[Int] = ...
scala> newFuture.value
res76: Option[scala.util.Try[Int]] = Some(Success(42))
Note that if a callback function passed to andThen throws an exception when
executed, that exception will not be propagated to subsequent callbacks or
reported via the resulting future.
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Other methods added in 2.12: flatten, zipWith, and transformWith
The flatten method, added in 2.12, transforms a Future nested inside another Future into a Future of the nested type. For example, flatten can
transform a Future[Future[Int]] into a Future[Int]:
scala> val nestedFuture = Future { Future { 42 } }
nestedFuture: Future[Future[Int]] = ...
scala> val flattened = nestedFuture.flatten // Scala 2.12
flattened: scala.concurrent.Future[Int] = Future(Success(42))
The zipWith method, added in 2.12, essentially zips two Futures together, then performs a map on the resulting tuple. Here’s an example of the
two-step process, a zip followed by a map:
scala> val futNum = Future { 21 + 21 }
futNum: scala.concurrent.Future[Int] = ...
scala> val futStr = Future { "ans" + "wer" }
futStr: scala.concurrent.Future[String] = ...
scala> val zipped = futNum zip futStr
zipped: scala.concurrent.Future[(Int, String)] = ...
scala> val mapped = zipped map {
case (num, str) => s"$num is the $str"
}
mapped: scala.concurrent.Future[String] = ...
scala> mapped.value
res2: Option[scala.util.Try[String]] =
Some(Success(42 is the answer))
The zipWith method allows you to perform the same operation in one step:
scala> val fut = futNum.zipWith(futStr) { // Scala 2.12
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case (num, str) => s"$num is the $str"
}
zipWithed: scala.concurrent.Future[String] = ...
scala> fut.value
res3: Option[scala.util.Try[String]] =
Some(Success(42 is the answer))
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Future also gained a transformWith method in Scala 2.12, which allows you to transform a future using a function from Try to Future. Here’s
an example:
scala> val flipped = success.transformWith { // Scala 2.12
case Success(res) =>
Future { throw new Exception(res.toString) }
case Failure(ex) => Future { 21 + 21 }
}
flipped: scala.concurrent.Future[Int] = ...
scala> flipped.value
res5: Option[scala.util.Try[Int]] =
Some(Failure(java.lang.Exception: 42))
The transformWith method is similar to the new, overloaded transform
method added in Scala 2.12, except instead of yielding a Try in your passed
function as in transform, transformWith allows you to yield a future.
32.4
Testing with Futures
One advantage of Scala’s futures is that they help you avoid blocking. On
most JVM implementations, after creating just a few thousand threads, the
cost of context switching between threads will degrade performance to an
unnacceptable level. By avoiding blocking, you can keep the finite number
of threads you decide to work with busy. Nevertheless, Scala does allow you
to block on a future result when you need to. Scala’s Await object facilitates
blocking to wait for future results. Here’s an example:
scala> import scala.concurrent.Await
import scala.concurrent.Await
scala> import scala.concurrent.duration._
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import scala.concurrent.duration._
scala> val fut = Future { Thread.sleep(10000); 21 + 21 }
fut: scala.concurrent.Future[Int] = ...
scala> val x = Await.result(fut, 15.seconds) // blocks
x: Int = 42
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Await.result takes a Future and a Duration. The Duration indicates
how long Await.result should wait for a Future to complete before timing
out. In this example, fifteen seconds was specified for the Duration. Thus
the Await.result method should not time out before the future completes
with its eventual value, 42.
One place where blocking has been generally accepted is in tests of asynchronous code. Now that the Await.result has returned, you can perform
a computation using that result, such as an assertion in a test:
scala> import org.scalatest.Matchers._
import org.scalatest.Matchers._
scala> x should be (42)
res0: org.scalatest.Assertion = Succeeded
Alternatively, you can use blocking constructs provided by ScalaTest’s trait
ScalaFutures. For example, the futureValue method, implicitly added
to Future by ScalaFutures, will block until the future completes. If the
future fails, futureValue will throw a TestFailedException describing
the problem. If the future succeeds, futureValue will return the successful
result of the future so you can perform asssertions on that value:
scala> import org.scalatest.concurrent.ScalaFutures._
import org.scalatest.concurrent.ScalaFutures._
scala> val fut = Future { Thread.sleep(10000); 21 + 21 }
fut: scala.concurrent.Future[Int] = ...
scala> fut.futureValue should be (42) // futureValue blocks
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res1: org.scalatest.Assertion = Succeeded
While blocking in tests is often fine, ScalaTest 3.0 adds “async” testing styles
that allow you to test futures without blocking. Given a future, instead of
blocking and performing assertions on the result, you can map assertions
directly onto that future and return the resulting Future[Assertion] to
ScalaTest. An example is shown in Listing 32.1. When the future assertion completes, ScalaTest will fire events (test succeeded, test failed, etc.) to
the test reporter asynchronously.
The async testing use case illustrates a general principle for working with
futures: Once in “future space,” try to stay in future space. Don’t block on a
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import org.scalatest.AsyncFunSpec
import scala.concurrent.Future
class AddSpec extends AsyncFunSpec {
def addSoon(addends: Int*): Future[Int] =
Future { addends.sum }
describe("addSoon") {
it("will eventually compute a sum of passed Ints") {
val futureSum: Future[Int] = addSoon(1, 2)
// You can map assertions onto a Future, then return
// the resulting Future[Assertion] to ScalaTest:
futureSum map { sum => assert(sum == 3) }
}
}
}
Listing 32.1 · Returning a future assertion to ScalaTest.
future then continue the computation with the result. Stay asynchronous by
performing a series of transformations, each of which returns a new future
to transform. To get results out of future space, register side effects to be
performed asynchronously once futures complete. This approach will help
you make maximum use of your threads.
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32.5
Conclusion
Concurrent programming gives you great power. It lets you simplify your
code and take advantage of multiple processors. It’s unfortunate that the
most widely used concurrency primitives, threads, locks, and monitors, are
such a minefield of deadlocks and race conditions. Futures provide a way
out of that minefield, letting you write concurrent programs without as great
a risk of deadlocks and race conditions. This chapter has introduced several
fundamental constructs for working with futures in Scala, including how to
create futures, how to transform them, and how to test them, among other
nuts and bolts. It then showed you how to use these constructs as part of a
general futures style.
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Combinator Parsing
Occasionally, you may need to process a small, special-purpose language.
For example, you may need to read configuration files for your software,
and you want to make them easier to modify by hand than XML. Alternatively, maybe you want to support an input language in your program, such
as search terms with boolean operators (computer, find me a movie “with
‘space ships’ and without ‘love stories”’). Whatever the reason, you are going to need a parser. You need a way to convert the input language into some
data structure your software can process.
Essentially, you have only a few choices. One choice is to roll your own
parser (and lexical analyzer). If you are not an expert, this is hard. If you are
an expert, it is still time consuming.
An alternative choice is to use a parser generator. There exist quite a few
of these generators. Some of the better known are Yacc and Bison for parsers
written in C and ANTLR for parsers written in Java. You’ll probably also
need a scanner generator such as Lex, Flex, or JFlex to go with it. This might
be the best solution, except for a couple of inconveniences. You need to learn
new tools, including their—sometimes obscure—error messages. You also
need to figure out how to connect the output of these tools to your program.
This might limit the choice of your programming language, and complicate
your tool chain.
This chapter presents a third alternative. Instead of using the standalone
domain specific language of a parser generator, you will use an internal domain specific language, or internal DSL for short. The internal DSL will
consist of a library of parser combinators—functions and operators defined
in Scala that will serve as building blocks for parsers. These building blocks
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will map one to one to the constructions of a context-free grammar, to make
them easy to understand.
This chapter introduces only one language feature that was not explained
before: this aliasing, in Section 33.6. The chapter does, however, heavily
use several other features that were explained in previous chapters. Among
others, parameterized types, abstract types, functions as objects, operator
overloading, by-name parameters, and implicit conversions all play important roles. The chapter shows how these language elements can be combined
in the design of a very high-level library.
The concepts explained in this chapter tend to be a bit more advanced
than previous chapters. If you have a good grounding in compiler construction, you’ll profit from it reading this chapter, because it will help you put
things better in perspective. However, the only prerequisite for understanding this chapter is that you know about regular and context-free grammars.
If you don’t, the material in this chapter can also safely be skipped.
33.1
Example: Arithmetic expressions
We’ll start with an example. Say you want to construct a parser for arithmetic
expressions consisting of floating-point numbers, parentheses, and the binary
operators +, -, *, and /. The first step is always to write down a grammar for
the language to be parsed. Here’s the grammar for arithmetic expressions:
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expr
term
factor
::=
::=
::=
term {"+" term | "-" term}.
factor {"*" factor | "/" factor}.
floatingPointNumber | "(" expr ")".
Here, | denotes alternative productions, and { . . . } denotes repetition (zero
or more times). And although there’s no use of it in this example, [ . . . ]
denotes an optional occurrence.
This context-free grammar defines formally a language of arithmetic expressions. Every expression (represented by expr) is a term, which can be
followed by a sequence of + or - operators and further terms. A term is a
factor, possibly followed by a sequence of * or / operators and further factors. A factor is either a numeric literal or an expression in parentheses.
Note that the grammar already encodes the relative precedence of operators.
For instance, * binds more tightly than +, because a * operation gives a term,
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whereas a + operation gives an expr, and exprs can contain terms but a term
can contain an expr only when the latter is enclosed in parentheses.
Now that you have defined the grammar, what’s next? If you use Scala’s
combinator parsers, you are basically done! You only need to perform some
systematic text replacements and wrap the parser in a class, as shown in
Listing 33.1:
import scala.util.parsing.combinator._
class Arith extends JavaTokenParsers {
def expr: Parser[Any] = term~rep("+"~term | "-"~term)
def term: Parser[Any] = factor~rep("*"~factor | "/"~factor)
def factor: Parser[Any] = floatingPointNumber | "("~expr~")"
}
Listing 33.1 · An arithmetic expression parser.
The parsers for arithmetic expressions are contained in a class that inherits
from the trait JavaTokenParsers. This trait provides the basic machinery
for writing a parser and also provides some primitive parsers that recognize
some word classes: identifiers, string literals and numbers. In the example
in Listing 33.1 you need only the primitive floatingPointNumber parser,
which is inherited from this trait.
The three definitions in class Arith represent the productions for arithmetic expressions. As you can see, they follow very closely the productions
of the context-free grammar. In fact, you could generate this part automatically from the context-free grammar, by performing a number of simple text
replacements:
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1. Every production becomes a method, so you need to prefix it with def.
2. The result type of each method is Parser[Any], so you need to change
the ::= symbol to “: Parser[Any] =”. You’ll find out later in this
chapter what the type Parser[Any] signifies, and also how to make it
more precise.
3. In the grammar, sequential composition was implicit, but in the program it is expressed by an explicit operator: ~. So you need to insert
a ~ between every two consecutive symbols of a production. In the
example in Listing 33.1 we chose not to write any spaces around the ~
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operator. That way, the parser code keeps closely to the visual appearance of the grammar—it just replaces spaces by ~ characters.
4. Repetition is expressed rep( . . . ) instead of { . . . }. Analogously
(though not shown in the example), option is expressed opt( . . . )
instead of [ . . . ].
5. The period (.) at the end of each production is omitted—you can, however, write a semicolon (;) if you prefer.
That’s all there is to it. The resulting class Arith defines three parsers,
expr, term and factor, which can be used to parse arithmetic expressions
and their parts.
33.2
Running your parser
You can exercise your parser with the following small program:
object ParseExpr extends Arith {
def main(args: Array[String]) = {
println("input : " + args(0))
println(parseAll(expr, args(0)))
}
}
The ParseExpr object defines a main method that parses the first commandline argument passed to it. It prints the original input argument, and then
prints its parsed version. Parsing is done by the expression:
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parseAll(expr, input)
This expression applies the parser, expr, to the given input. It expects that
all of the input matches, i.e., that there are no characters trailing a parsed
expression. There’s also a method parse, which allows you to parse an
input prefix, leaving some remainder unread.
You can run the arithmetic parser with the following command:
$ scala ParseExpr "2 * (3 + 7)"
input: 2 * (3 + 7)
[1.12] parsed: ((2~List((*~(((~((3~List())~List((+
~(7~List())))))~)))))~List())
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The output tells you that the parser successfully analyzed the input string up
to position [1.12]. That means the first line and the twelfth column—in other
words, the whole input string—was parsed. Disregard for the moment the
result after “parsed:”. It is not very useful, and you will find out later how
to get more specific parser results.
You can also try to introduce some input string that is not a legal expression. For instance, you could write one closing parenthesis too many:
$ scala ParseExpr "2 * (3 + 7))"
input: 2 * (3 + 7))
[1.12] failure: `-' expected but `)' found
2 * (3 + 7))
ˆ
Here, the expr parser parsed everything until the final closing parenthesis, which does not form part of the arithmetic expression. The parseAll
method then issued an error message, which said that it expected a - operator at the point of the closing parenthesis. You’ll find out later in this chapter
why it produced this particular error message, and how you can improve it.
33.3
Basic regular expression parsers
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The parser for arithmetic expressions made use of another parser, named
floatingPointNumber. This parser, which was inherited from Arith’s supertrait, JavaTokenParsers, recognizes a floating point number in the format of Java. But what do you do if you need to parse numbers in a format
that’s a bit different from Java’s? In this situation, you can use a regular
expression parser.
The idea is that you can use any regular expression as a parser. The
regular expression parses all strings that it can match. Its result is the parsed
string. For instance, the regular expression parser shown in Listing 33.2
describes Java’s identifiers:
object MyParsers extends RegexParsers {
val ident: Parser[String] = """[a-zA-Z_]\w*""".r
}
Listing 33.2 · A regular expression parser for Java identifiers.
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The MyParsers object of Listing 33.2 inherits from trait RegexParsers,
whereas Arith inherited from JavaTokenParsers. Scala’s parsing combinators are arranged in a hierarchy of traits, which are all contained in package
scala.util.parsing.combinator. The top-level trait is Parsers, which
defines a very general parsing framework for all sorts of input. One level
below is trait RegexParsers, which requires that the input is a sequence of
characters and provides for regular expression parsing. Even more specialized is trait JavaTokenParsers, which implements parsers for basic classes
of words (or tokens) as they are defined in Java.
33.4
Another example: JSON
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JSON, the JavaScript Object Notation, is a popular data interchange format.
In this section, we’ll show you how to write a parser for it. Here’s a grammar
that describes the syntax of JSON:
value
::=
obj
arr
members
member
values
::=
::=
::=
::=
::=
obj | arr | stringLiteral |
floatingPointNumber |
"null" | "true" | "false".
"{" [ members ] "}".
"[" [ values ] "]".
member {"," member}.
stringLiteral ":" value.
value {"," value}.
A JSON value is an object, array, string, number, or one of the three reserved words null, true, or false. A JSON object is a (possibly empty)
sequence of members separated by commas and enclosed in braces. Each
member is a string/value pair where the string and the value are separated by
a colon. Finally, a JSON array is a sequence of values separated by commas
and enclosed in square brackets. As an example, Listing 33.3 contains an
address-book formatted as a JSON object.
Parsing such data is straightforward when using Scala’s parser combinators. The complete parser is shown in Listing 33.4. This parser follows
the same structure as the arithmetic expression parser. It is again a straightforward mapping of the productions of the JSON grammar. The productions
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{
"address book": {
"name": "John Smith",
"address": {
"street": "10 Market Street",
"city"
: "San Francisco, CA",
"zip"
: 94111
},
"phone numbers": [
"408 338-4238",
"408 111-6892"
]
}
}
Listing 33.3 · Data in JSON format.
use one shortcut that simplifies the grammar: The repsep combinator parses
a (possibly empty) sequence of terms that are separated by a given separator
string. For instance, in the example in Listing 33.4, repsep(member, ",")
parses a comma-separated sequence of member terms. Otherwise, the productions in the parser correspond exactly to the productions in the grammar,
as was the case for the arithmetic expression parsers.
To try out the JSON parsers, we’ll change the framework a bit, so that
the parser operates on a file instead of on the command line:
import java.io.FileReader
object ParseJSON extends JSON {
def main(args: Array[String]) = {
val reader = new FileReader(args(0))
println(parseAll(value, reader))
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}
}
The main method in this program first creates a FileReader object. It
then parses the characters returned by that reader according to the value
production of the JSON grammar. Note that parseAll and parse exist in
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overloaded variants: both can take a character sequence or alternatively an
input reader as second argument.
If you store the “address book” object shown in Listing 33.3 into a file
named address-book.json and run the ParseJSON program on it, you
should get:
$ scala ParseJSON address-book.json
[13.4] parsed: (({~List((("address book"~:)~(({~List(((
"name"~:)~"John Smith"), (("address"~:)~(({~List(((
"street"~:)~"10 Market Street"), (("city"~:)~"San Francisco
,CA"), (("zip"~:)~94111)))~})), (("phone numbers"~:)~(([~
List("408 338-4238", "408 111-6892"))~]))))~}))))~})
33.5
Parser output
The ParseJSON program successfully parsed the JSON address book. However, the parser output looks strange. It seems to be a sequence composed
of bits and pieces of the input glued together with lists and ~ combinations.
This output is not very useful. It is less readable for humans than the input,
but it is also too disorganized to be easily analyzable by a computer. It’s time
to do something about this.
import scala.util.parsing.combinator._
class JSON extends JavaTokenParsers {
def value : Parser[Any] = obj | arr |
stringLiteral |
floatingPointNumber |
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"null" | "true" | "false"
def obj
: Parser[Any] = "{"~repsep(member, ",")~"}"
def arr
: Parser[Any] = "["~repsep(value, ",")~"]"
def member: Parser[Any] = stringLiteral~":"~value
}
Listing 33.4 · A simple JSON parser.
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To figure out what to do, you need to know first what the individual
parsers in the combinator frameworks return as a result (provided they succeed in parsing the input). Here are the rules:
1. Each parser written as a string (such as: "{" or ":" or "null") returns
the parsed string itself.
2. Regular expression parsers such as """[a-zA-Z_]\w*""".r also return the parsed string itself. The same holds for regular expression
parsers such as stringLiteral or floatingPointNumber, which are
inherited from trait JavaTokenParsers.
3. A sequential composition P~Q returns the results of both P and of Q.
These results are returned in an instance of a case class that is also
written ~. So if P returns "true" and Q returns "?", then the sequential
composition P~Q returns ~("true", "?"), which prints as (true~?).
4. An alternative composition P | Q returns the result of either P or Q,
whichever one succeeds.
5. A repetition rep(P) or repsep(P, separator) returns a list of the
results of all runs of P.
6. An option opt(P) returns an instance of Scala’s Option type. It returns Some(R) if P succeeds with result R and None if P fails.
With these rules you can now deduce why the parser output appeared as it did
in the previous examples. However, the output is still not very convenient.
It would be much better to map a JSON object into an internal Scala representation that represents the meaning of the JSON value. A more natural
representation would be as follows:
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• A JSON object is represented as a Scala map of type Map[String,
Any]. Every member is represented as a key/value binding in the map.
• A JSON array is represented as a Scala list of type List[Any].
• A JSON string is represented as a Scala String.
• A JSON numeric literal is represented as a Scala Double.
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Section 33.5
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• The values true, false, and null are represented as the Scala values
with the same names.
To produce this representation, you need to make use of one more combination form for parsers: ˆˆ.
The ˆˆ operator transforms the result of a parser. Expressions using this
operator have the form P ˆˆ f where P is a parser and f is a function. P ˆˆ f
parses the same sentences as just P. Whenever P returns with some result R,
the result of P ˆˆ f is f(R).
As an example, here is a parser that parses a floating point number and
converts it to a Scala value of type Double:
floatingPointNumber ˆˆ (_.toDouble)
And here is a parser that parses the string "true" and returns Scala’s boolean
true value:
"true" ˆˆ (x => true)
Now for more advanced transformations. Here’s a new version of a parser
for JSON objects that returns a Scala Map:
def obj: Parser[Map[String, Any]] = // Can be improved
"{"~repsep(member, ",")~"}" ˆˆ
{ case "{"~ms~"}" => Map() ++ ms }
Remember that the ~ operator produces as its result an instance of a case
class with the same name: ~. Here’s a definition of that class—it’s an inner
class of trait Parsers:
case class ~[+A, +B](x: A, y: B) {
override def toString = "(" + x + "~" + y + ")"
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}
The name of the class is intentionally the same as the name of the sequence
combinator method, ~. That way, you can match parser results with patterns that follow the same structure as the parsers themselves. For instance,
the pattern "{"~ms~"}" matches a result string "{" followed by a result
variable ms, which is followed in turn by a result string "}". This pattern
corresponds exactly to what is returned by the parser on the left of the ˆˆ.
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In its desugared versions where the ~ operator comes first, the same pattern
reads ~(~("{", ms), "}"), but this is much less legible.
The purpose of the "{"~ms~"}" pattern is to strip off the braces so that
you can get at the list of members resulting from the repsep(member, ",")
parser. In cases like these there is also an alternative that avoids producing unnecessary parser results that are immediately discarded by the pattern
match. The alternative makes use of the ~> and <~ parser combinators. Both
express sequential composition like ~, but ~> keeps only the result of its right
operand, whereas <~ keeps only the result of its left operand. Using these
combinators, the JSON object parser can be expressed more succinctly:
def obj: Parser[Map[String, Any]] =
"{"~> repsep(member, ",") <~"}" ˆˆ (Map() ++ _)
Listing 33.5 shows a full JSON parser that returns meaningful results. If
you run this parser on the address-book.json file, you will get the following result (after adding some newlines and indentation):
$ scala JSON1Test address-book.json
[14.1] parsed: Map(
address book -> Map(
name -> John Smith,
address -> Map(
street -> 10 Market Street,
city -> San Francisco, CA,
zip -> 94111),
phone numbers -> List(408 338-4238, 408 111-6892)
)
)
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This is all you need to know in order to get started writing your own
parsers. As an aide to memory, Table 33.1 lists the parser combinators that
were discussed so far.
Symbolic versus alphanumeric names
Many of the parser combinators in Table 33.1 use symbolic names. This
has both advantages and disadvantages. On the minus side, symbolic names
take time to learn. Users who are unfamiliar with Scala’s combinator parsing
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import scala.util.parsing.combinator._
class JSON1 extends JavaTokenParsers {
def obj: Parser[Map[String, Any]] =
"{"~> repsep(member, ",") <~"}" ˆˆ (Map() ++ _)
def arr: Parser[List[Any]] =
"["~> repsep(value, ",") <~"]"
def member: Parser[(String, Any)] =
stringLiteral~":"~value ˆˆ
{ case name~":"~value => (name, value) }
def value: Parser[Any] = (
obj
| arr
| stringLiteral
| floatingPointNumber ˆˆ (_.toDouble)
| "null"
ˆˆ (x => null)
| "true"
ˆˆ (x => true)
| "false" ˆˆ (x => false)
)
}
Listing 33.5 · A full JSON parser that returns meaningful results.
Table 33.1 · Summary of parser combinators
"..."
"...".r
P~Q
P <~ Q, P ~> Q
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P|Q
opt(P)
rep(P)
repsep(P, Q)
P ˆˆ f
literal
regular expression
sequential composition
sequential composition; keep left/right only
alternative
option
repetition
interleaved repetition
result conversion
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Turning off semicolon inference
Note that the body of the value parser in Listing 33.5 is enclosed in
parentheses. This is a little trick to disable semicolon inference in
parser expressions. You saw in Section 4.2 that Scala assumes there’s
a semicolon between any two lines that can be separate statements
syntactically, unless the first line ends in an infix operator, or the two
lines are enclosed in parentheses or square brackets. Now, you could
have written the | operator at the end of the each alternative instead of
at the beginning of the following one, like this:
def value: Parser[Any] =
obj |
arr |
stringLiteral |
...
In that case, no parentheses around the body of the value parser would
have been required. However, some people prefer to see the | operator
at the beginning of the second alternative rather than at the end of the
first. Normally, this would lead to an unwanted semicolon between the
two lines, like this:
obj;
// semicolon implicitly inserted
| arr
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The semicolon changes the structure of the code, causing it to fail
compilation. Putting the whole expression in parentheses avoids the
semicolon and makes the code compile correctly.
libraries are probably mystified what ~, ~>, or ˆˆ mean. On the plus side,
symbolic names are short, and can be chosen to have the “right” precedences
and associativities. For instance, the parser combinators ~, ˆˆ, and | are
chosen intentionally in decreasing order of precedence. A typical grammar
production is composed of alternatives that have a parsing part and a transformation part. The parsing part usually contains several sequential items
separated by ~ operators. With the chosen precedences of ~, ˆˆ, and | you
can write such a grammar production without needing any parentheses.
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Furthermore, symbolic operators take less visual real estate than alphabetic ones. That’s important for a parser because it lets you concentrate on
the grammar at hand, instead of the combinators themselves. To see the difference, imagine for a moment that sequential composition (~) was called
andThen and alternative (|) was called orElse. The arithmetic expression
parsers in Listing 33.1 on page 738 would look as follows:
class ArithHypothetical extends JavaTokenParsers {
def expr: Parser[Any]
=
term andThen rep(("+" andThen term) orElse
("-" andThen term))
def term: Parser[Any]
=
factor andThen rep(("*" andThen factor) orElse
("/" andThen factor))
def factor: Parser[Any] =
floatingPointNumber orElse
("(" andThen expr andThen ")")
}
You notice that the code becomes much longer, and that it’s hard to “see”
the grammar among all those operators and parentheses. On the other hand,
somebody new to combinator parsing could probably figure out better what
the code is supposed to do.
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33.6
Implementing combinator parsers
The previous sections have shown that Scala’s combinator parsers provide a
convenient means for constructing your own parsers. Since they are nothing
more than a Scala library, they fit seamlessly into your Scala programs. So
it’s very easy to combine a parser with some code that processes the results it
delivers, or to rig a parser so that it takes its input from some specific source
(say, a file, a string, or a character array).
How is this achieved? In the rest of this chapter you’ll take a look “under
the hood” of the combinator parser library. You’ll see what a parser is, and
how the primitive parsers and parser combinators encountered in previous
sections are implemented. You can safely skip these parts if all you want to
do is write some simple combinator parsers. On the other hand, reading the
rest of this chapter should give you a deeper understanding of combinator
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Choosing between symbolic and alphabetic names
As guidelines for choosing between symbolic and alphabetic names we
recommend the following:
• Use symbolic names in cases where they already have a universally established meaning. For instance, nobody would
recommend writing add instead of + for numeric addition.
• Otherwise, give preference to alphabetic names if you want your
code to be understandable to casual readers.
• You can still choose symbolic names for domain-specific libraries, if this gives clear advantages in legibility and you do not
expect anyway that a casual reader without a firm grounding in
the domain would be able to understand the code immediately.
In the case of parser combinators we are looking at a highly domainspecific language, which casual readers may have trouble understanding
even with alphabetic names. Furthermore, symbolic names give clear
advantages in legibility for the expert. So we believe their use is warranted in this application.
parsers in particular, and of the design principles of a combinator domainspecific language in general.
The core of Scala’s combinator parsing framework is contained in the
trait scala.util.parsing.combinator.Parsers. This trait defines the
Parser type as well as all fundamental combinators. Except where stated
explicitly otherwise, the definitions explained in the following two subsections all reside in this trait. That is, they are assumed to be contained in a
trait definition that starts as follows:
package scala.util.parsing.combinator
trait Parsers {
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... // code goes here unless otherwise stated
}
A Parser is in essence just a function from some input type to a parse result.
As a first approximation, the type could be written as follows:
type Parser[T] = Input => ParseResult[T]
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Parser input
Sometimes, a parser reads a stream of tokens instead of a raw sequence of
characters. A separate lexical analyzer is then used to convert a stream of
raw characters into a stream of tokens. The type of parser inputs is defined
as follows:
type Input = Reader[Elem]
The class Reader comes from the package scala.util.parsing.input. It
is similar to a Stream, but also keeps track of the positions of all the elements
it reads. The type Elem represents individual input elements. It is an abstract
type member of the Parsers trait:
type Elem
This means that subclasses and subtraits of Parsers need to instantiate class
Elem to the type of input elements that are being parsed. For instance,
RegexParsers and JavaTokenParsers fix Elem to be equal to Char. But
it would also be possible to set Elem to some other type, such as the type of
tokens returned from a separate lexer.
Parser results
A parser might either succeed or fail on some given input. Consequently
class ParseResult has two subclasses for representing success and failure:
sealed abstract class ParseResult[+T]
case class Success[T](result: T, in: Input)
extends ParseResult[T]
case class Failure(msg: String, in: Input)
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extends ParseResult[Nothing]
The Success case carries the result returned from the parser in its result
parameter. The type of parser results is arbitrary; that’s why ParseResult,
Success, and Parser are all parameterized with a type parameter T. The
type parameter represents the kinds of results returned by a given parser.
Success also takes a second parameter, in, which refers to the input immediately following the part that the parser consumed. This field is needed for
chaining parsers, so that one parser can operate after another. Note that this
is a purely functional approach to parsing. Input is not read as a side effect,
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but it is kept in a stream. A parser analyzes some part of the input stream,
and then returns the remaining part in its result.
The other subclass of ParseResult is Failure. This class takes as a
parameter a message that describes why the parser failed. Like Success,
Failure also takes the remaining input stream as a second parameter. This
is needed not for chaining (the parser won’t continue after a failure), but to
position the error message at the correct place in the input stream.
Note that parse results are defined to be covariant in the type parameter
T. That is, a parser returning Strings as result, say, is compatible with a
parser returning AnyRefs.
The Parser class
The previous characterization of parsers as functions from inputs to parse
results was a bit oversimplified. The previous examples showed that parsers
also implement methods such as ~ for sequential composition of two parsers
and | for their alternative composition. So Parser is in reality a class that
inherits from the function type Input => ParseResult[T] and additionally
defines these methods:
abstract class Parser[+T] extends (Input => ParseResult[T])
{ p =>
// An unspecified method that defines
// the behavior of this parser.
def apply(in: Input): ParseResult[T]
def ~ ...
def | ...
...
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}
Since parsers are (i.e., inherit from) functions, they need to define an apply
method. You see an abstract apply method in class Parser, but this is just
for documentation, as the same method is in any case inherited from the
parent type Input => ParseResult[T] (recall that this type is an abbreviation for scala.Function1[Input, ParseResult[T]]). The apply method
still needs to be implemented in the individual parsers that inherit from the
abstract Parser class. These parsers will be discussed after the following
section on this aliasing.
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Aliasing this
The body of the Parser class starts with a curious expression:
abstract class Parser[+T] extends ... { p =>
A clause such as “id =>” immediately after the opening brace of a class
template defines the identifier id as an alias for this in the class. It’s as if
you had written:
val id = this
in the class body, except that the Scala compiler knows that id is an alias
for this. For instance, you could access an object-private member m of the
class using either id.m or this.m; the two are completely equivalent. The
first expression would not compile if id were just defined as a val with this
as its right hand side, because in that case the Scala compiler would treat id
as a normal identifier.
You saw syntax like this in Section 29.4, where it was used to give a self
type to a trait. Aliasing can also be a good abbreviation when you need to
access the this of an outer class. Here’s an example:
class Outer { outer =>
class Inner {
println(Outer.this eq outer) // prints: true
}
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}
The example defines two nested classes, Outer and Inner. Inside Inner
the this value of the Outer class is referred to twice, using different expressions. The first expression shows the Java way of doing things: You can
prefix the reserved word this with the name of an outer class and a period;
such an expression then refers to the this of the outer class. The second expression shows the alternative that Scala gives you. By introducing an alias
named outer for this in class Outer, you can refer to this alias directly
also in inner classes. The Scala way is more concise, and can also improve
clarity, if you choose the name of the alias well. You’ll see examples of this
in pages 754 and 755.
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Single-token parsers
Trait Parsers defines a generic parser elem that can be used to parse any
single token:
def elem(kind: String, p: Elem => Boolean) =
new Parser[Elem] {
def apply(in: Input) =
if (p(in.first)) Success(in.first, in.rest)
else Failure(kind + " expected", in)
}
This parser takes two parameters: a kind string describing what kind of
token should be parsed and a predicate p on Elems, which indicates whether
an element fits the class of tokens to be parsed.
When applying the parser elem(kind, p) to some input in, the first
element of the input stream is tested with predicate p. If p returns true, the
parser succeeds. Its result is the element itself, and its remaining input is
the input stream starting just after the element that was parsed. On the other
hand, if p returns false, the parser fails with an error message that indicates
what kind of token was expected.
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Sequential composition
The elem parser only consumes a single element. To parse more interesting phrases, you can string parsers together with the sequential composition
operator ~. As you have seen before, P~Q is a parser that applies first the P
parser to a given input string. Then, if P succeeds, the Q parser is applied to
the input that’s left after P has done its job.
The ~ combinator is implemented as a method in class Parser. Its definition is shown in Listing 33.6. The method is a member of the Parser
class. Inside this class, p is specified by the “p =>” part as an alias of this,
so p designates the left operand (or: receiver) of ~. Its right operand is represented by parameter q. Now, if p~q is run on some input in, first p is run
on in and the result is analyzed in a pattern match. If p succeeds, q is run on
the remaining input in1. If q also succeeds, the parser as a whole succeeds.
Its result is a ~ object containing both the result of p (i.e., x) and the result
of q (i.e., y). On the other hand, if either p or q fails the result of p~q is the
Failure object returned by p or q.
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abstract class Parser[+T] ... { p =>
...
def ~ [U](q: => Parser[U]) = new Parser[T~U] {
def apply(in: Input) = p(in) match {
case Success(x, in1) =>
q(in1) match {
case Success(y, in2) => Success(new ~(x, y), in2)
case failure => failure
}
case failure => failure
}
}
Listing 33.6 · The ~ combinator method.
The result type of ~ is a parser that returns an instance of the case class
~ with elements of types T and U. The type expression T~U is just a more
legible shorthand for the parameterized type ~[T, U]. Generally, Scala always interprets a binary type operation such as A op B, as the parameterized
type op[A, B]. This is analogous to the situation for patterns, where a binary
pattern P op Q is also interpreted as an application, i.e., op(P, Q).
The other two sequential composition operators, <~ and ~>, could be
defined just like ~, only with some small adjustment in how the result is
computed. A more elegant technique, though, is to define them in terms of ~
as follows:
def <~ [U](q: => Parser[U]): Parser[T] =
(p~q) ˆˆ { case x~y => x }
def ~> [U](q: => Parser[U]): Parser[U] =
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(p~q) ˆˆ { case x~y => y }
Alternative composition
An alternative composition P | Q applies either P or Q to a given input. It
first tries P. If P succeeds, the whole parser succeeds with the result of P.
Otherwise, if P fails, then Q is tried on the same input as P. The result of Q is
then the result of the whole parser.
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Here is a definition of | as a method of class Parser:
def | (q: => Parser[T]) = new Parser[T] {
def apply(in: Input) = p(in) match {
case s1 @ Success(_, _) => s1
case failure => q(in)
}
}
Note that if P and Q both fail, then the failure message is determined by Q.
This subtle choice is discussed later, in Section 33.9.
Dealing with recursion
Note that the q parameter in methods ~ and | is by-name—its type is preceded by =>. This means that the actual parser argument will be evaluated
only when q is needed, which should only be the case after p has run. This
makes it possible to write recursive parsers like the following one which
parses a number enclosed by arbitrarily many parentheses:
def parens = floatingPointNumber | "("~parens~")"
If | and ~ took by-value parameters, this definition would immediately cause
a stack overflow without reading anything, because the value of parens occurs in the middle of its right-hand side.
Result conversion
The last method of class Parser converts a parser’s result. The parser P ˆˆ f
succeeds exactly when P succeeds. In that case it returns P’s result converted
using the function f. Here is the implementation of this method:
def ˆˆ [U](f: T => U): Parser[U] = new Parser[U] {
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def apply(in: Input) = p(in) match {
case Success(x, in1) => Success(f(x), in1)
case failure => failure
}
}
} // end Parser
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Parsers that don’t read any input
There are also two parsers that do not consume any input: success and
failure. The parser success(result) always succeeds with the given
result. The parser failure(msg) always fails with error message msg.
Both are implemented as methods in trait Parsers, the outer trait that also
contains class Parser:
def success[T](v: T) = new Parser[T] {
def apply(in: Input) = Success(v, in)
}
def failure(msg: String) = new Parser[Nothing] {
def apply(in: Input) = Failure(msg, in)
}
Option and repetition
Also defined in trait Parsers are the option and repetition combinators opt,
rep, and repsep. They are all implemented in terms of sequential composition, alternative, and result conversion:
def opt[T](p: => Parser[T]): Parser[Option[T]] = (
p ˆˆ Some(_)
| success(None)
)
def rep[T](p: => Parser[T]): Parser[List[T]] = (
p~rep(p) ˆˆ { case x~xs => x :: xs }
| success(List())
)
def repsep[T](p: => Parser[T],
q: => Parser[Any]): Parser[List[T]] = (
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p~rep(q~> p) ˆˆ { case r~rs => r :: rs }
| success(List())
)
} // end Parsers
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Section 33.7
33.7
Chapter 33 · Combinator Parsing
String literals and regular expressions
The parsers you saw so far made use of string literals and regular expressions
to parse single words. The support for these comes from RegexParsers, a
subtrait of Parsers:
trait RegexParsers extends Parsers {
This trait is more specialized than trait Parsers in that it only works for
inputs that are sequences of characters:
type Elem = Char
It defines two methods, literal and regex, with the following signatures:
implicit def literal(s: String): Parser[String] = ...
implicit def regex(r: Regex): Parser[String] = ...
Note that both methods have an implicit modifier, so they are automatically applied whenever a String or Regex is given but a Parser is expected. That’s why you can write string literals and regular expressions directly in a grammar, without having to wrap them with one of these methods.
For instance, the parser "("~expr~")" will be automatically expanded to
literal("(")~expr~literal(")").
The RegexParsers trait also takes care of handling white space between
symbols. To do this, it calls a method named handleWhiteSpace before running a literal or regex parser. The handleWhiteSpace method skips the
longest input sequence that conforms to the whiteSpace regular expression,
which is defined by default as follows:
protected val whiteSpace = """\s+""".r
} // end RegexParsers
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If you prefer a different treatment of white space, you can override the
whiteSpace val. For instance, if you want white space not to be skipped at
all, you can override whiteSpace with the empty regular expression:
object MyParsers extends RegexParsers {
override val whiteSpace = "".r
...
}
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Section 33.8
33.8
Chapter 33 · Combinator Parsing
Lexing and parsing
The task of syntax analysis is often split into two phases. The lexer phase
recognizes individual words in the input and classifies them into some token
classes. This phase is also called lexical analysis. This is followed by a
syntactical analysis phase that analyzes sequences of tokens. Syntactical
analysis is also sometimes just called parsing, even though this is slightly
imprecise, as lexical analysis can also be regarded as a parsing problem.
The Parsers trait as described in the previous section can be used for
either phase, because its input elements are of the abstract type Elem. For
lexical analysis, Elem would be instantiated to Char, meaning the individual
characters that make up a word are being parsed. The syntactical analyzer
would in turn instantiate Elem to the type of token returned by the lexer.
Scala’s parsing combinators provide several utility classes for lexical and
syntactic analysis. These are contained in two sub-packages, one for each
kind of analysis:
scala.util.parsing.combinator.lexical
scala.util.parsing.combinator.syntactical
If you want to split your parser into a separate lexer and syntactical analyzer,
you should consult the Scaladoc documentation for these packages. But for
simple parsers, the regular expression based approach shown previously in
this chapter is usually sufficient.
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33.9
Error reporting
There’s one final topic that was not covered yet: how does the parser issue an
error message? Error reporting for parsers is somewhat of a black art. One
problem is that when a parser rejects some input, it generally has encountered many different failures. Each alternative parse must have failed, and
recursively so at each choice point. Which of the usually numerous failures
should be emitted as error message to the user?
Scala’s parsing library implements a simple heuristic: among all failures,
the one that occurred at the latest position in the input is chosen. In other
words, the parser picks the longest prefix that is still valid and issues an
error message that describes why parsing the prefix could not be continued
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further. If there are several failure points at that latest position, the one that
was visited last is chosen.
For instance, consider running the JSON parser on a faulty address book
which starts with the line:
{ "name": John,
The longest legal prefix of this phrase is “{ "name": ”. So the JSON parser
will flag the word John as an error. The JSON parser expects a value at this
point, but John is an identifier, which does not count as a value (presumably,
the author of the document had forgotten to enclose the name in quotation
marks). The error message issued by the parser for this document is:
[1.13] failure: "false" expected but identifier John found
{ "name": John,
ˆ
The part that “false” was expected comes from the fact that "false" is the
last alternative of the production for value in the JSON grammar. So this
was the last failure at this point. Users who know the JSON grammar in detail
can reconstruct the error message, but for non-experts this error message is
probably surprising and can also be quite misleading.
A better error message can be engineered by adding a “catch-all” failure
point as last alternative of a value production:
def value: Parser[Any] =
obj | arr | stringLit | floatingPointNumber | "null" |
"true" | "false" | failure("illegal start of value")
This addition does not change the set of inputs that are accepted as valid
documents. What it does is improve the error messages, because now it will
be the explicitly added failure that comes as last alternative and therefore
gets reported:
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[1.13] failure: illegal start of value
{ "name": John,
ˆ
The implementation of the “latest possible” scheme of error reporting uses a
field named lastFailure in trait Parsers to mark the failure that occurred
at the latest position in the input:
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var lastFailure: Option[Failure] = None
The field is initialized to None. It is updated in the constructor of the Failure
class:
case class Failure(msg: String, in: Input)
extends ParseResult[Nothing] {
if (lastFailure.isDefined &&
lastFailure.get.in.pos <= in.pos)
lastFailure = Some(this)
}
The field is read by the phrase method, which emits the final error message
if the parser failed. Here is the implementation of phrase in trait Parsers:
def phrase[T](p: Parser[T]) = new Parser[T] {
lastFailure = None
def apply(in: Input) = p(in) match {
case s @ Success(out, in1) =>
if (in1.atEnd) s
else Failure("end of input expected", in1)
case f : Failure =>
lastFailure
}
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}
The phrase method runs its argument parser p. If p succeeds with a completely consumed input, the success result of p is returned. If p succeeds
but the input is not read completely, a failure with message “end of input
expected” is returned. If p fails, the failure or error stored in lastFailure
is returned. Note that the treatment of lastFailure is non-functional; it is
updated as a side effect by the constructor of Failure and by the phrase
method itself. A functional version of the same scheme would be possible,
but it would require threading the lastFailure value through every parser
result, no matter whether this result is a Success or a Failure.
33.10
Backtracking versus LL(1)
The parser combinators employ backtracking to choose between different
parsers in an alternative. In an expression P | Q, if P fails, then Q is run on
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the same input as P. This happens even if P has parsed some tokens before
failing. In this case the same tokens will be parsed again by Q.
Backtracking imposes only a few restrictions on how to formulate a
grammar so that it can be parsed. Essentially, you just need to avoid leftrecursive productions. A production such as:
expr
::=
expr "+" term | term.
will always fail because expr immediately calls itself and thus never progresses any further.1 On the other hand, backtracking is potentially costly
because the same input can be parsed several times. Consider for instance
the production:
expr
::=
term "+" expr | term.
What happens if the expr parser is applied to an input such as (1 + 2) * 3
which constitutes a legal term? The first alternative would be tried, and
would fail when matching the + sign. Then the second alternative would be
tried on the same term and this would succeed. In the end the term ended up
being parsed twice.
It is often possible to modify the grammar so that backtracking can be
avoided. For instance, in the case of arithmetic expressions, either one of the
following productions would work:
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expr
expr
::=
::=
term [ "+" expr ].
term {"+" term}.
Many languages admit so-called “LL(1)” grammars.2 When a combinator
parser is formed from such a grammar, it will never backtrack, i.e., the input
position will never be reset to an earlier value. For instance, the grammars
for arithmetic expressions and JSON terms earlier in this chapter are both
LL(1), so the backtracking capabilities of the parser combinator framework
are never exercised for inputs from these languages.
The combinator parsing framework allows you to express the expectation
that a grammar is LL(1) explicitly, using a new operator ~!. This operator is
1 There
are ways to avoid stack overflows even in the presence of left-recursion, but this
requires a more refined parsing combinator framework, which to date has not been implemented.
2 Aho, et al., Compilers: Principles, Techniques, and Tools. [Aho86]
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like sequential composition ~ but it will never backtrack to “un-read” input
elements that have already been parsed. Using this operator, the productions
in the arithmetic expression parser could alternatively be written as follows:
def expr
: Parser[Any] =
term ~! rep("+" ~! term | "-" ~! term)
def term
: Parser[Any] =
factor ~! rep("*" ~! factor | "/" ~! factor)
def factor: Parser[Any] =
"(" ~! expr ~! ")" | floatingPointNumber
One advantage of an LL(1) parser is that it can use a simpler input technique.
Input can be read sequentially, and input elements can be discarded once they
are read. That’s another reason why LL(1) parsers are usually more efficient
than backtracking parsers.
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33.11
Conclusion
You have now seen all the essential elements of Scala’s combinator parsing
framework. It’s surprisingly little code for something that’s genuinely useful.
With the framework you can construct parsers for a large class of contextfree grammars. The framework lets you get started quickly, but it is also
customizable to new kinds of grammars and input methods. Being a Scala
library, it integrates seamlessly with the rest of the language. So it’s easy to
integrate a combinator parser in a larger Scala program.
One downside of combinator parsers is that they are not very efficient, at
least not when compared with parsers generated from special purpose tools
such as Yacc or Bison. There are two reasons for this. First, the backtracking
method used by combinator parsing is itself not very efficient. Depending on
the grammar and the parse input, it might yield an exponential slow-down
due to repeated backtracking. This can be fixed by making the grammar
LL(1) and by using the committed sequential composition operator, ~!.
The second problem affecting the performance of combinator parsers
is that they mix parser construction and input analysis in the same set of
operations. In effect, a parser is generated anew for each input that’s parsed.
This problem can be overcome, but it requires a different implementation
of the parser combinator framework. In an optimizing framework, a parser
would no longer be represented as a function from inputs to parse results.
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Instead, it would be represented as a tree, where every construction step was
represented as a case class. For instance, sequential composition could be
represented by a case class Seq, alternative by Alt, and so on. The “outermost” parser method, phrase, could then take this symbolic representation
of a parser and convert it to highly efficient parsing tables, using standard
parser generator algorithms.
What’s nice about all this is that from a user perspective nothing changes
compared to plain combinator parsers. Users still write parsers in terms of
ident, floatingPointNumber, ~, |, and so on. They need not be aware
that these methods generate a symbolic representation of a parser instead of a
parser function. Since the phrase combinator converts these representations
into real parsers, everything works as before.
The advantage of this scheme with respect to performance is two-fold.
First, you can now factor out parser construction from input analysis. If you
were to write:
val jsonParser = phrase(value)
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and then apply jsonParser to several different inputs, the jsonParser
would be constructed only once, not every time an input is read.
Second, the parser generation can use efficient parsing algorithms such
as LALR(1).3 These algorithms usually lead to much faster parsers than
parsers that operate with backtracking.
At present, such an optimizing parser generator has not yet been written
for Scala. But it would be perfectly possible to do so. If someone contributes
such a generator, it will be easy to integrate into the standard Scala library.
Even postulating that such a generator will exist at some point in the future, however, there are reasons for keeping the current parser combinator
framework around. It is much easier to understand and to adapt than a parser
generator, and the difference in speed would often not matter in practice,
unless you want to parse very large inputs.
3 Aho,
et al., Compilers: Principles, Techniques, and Tools. [Aho86]
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Chapter 34
GUI Programming
In this chapter you’ll learn how to develop in Scala applications that use a
graphical user interface (GUI). The applications we’ll develop are based on a
Scala library that provides access to Java’s Swing framework of GUI classes.
Conceptually, the Scala library resembles the underlying Swing classes, but
hides much of their complexity. You’ll find out that developing GUI applications using the framework is actually quite easy.
Even with Scala’s simplifications, a framework like Swing is quite rich,
with many different classes and many methods in each class. To find your
way in such a rich library, it helps to use an IDE such as Scala’s Eclipse
plugin. The advantage is that the IDE can show you interactively with its
command completion which classes are available in a package and which
methods are available for objects you reference. This speeds up your learning
considerably when you first explore an unknown library space.
34.1
A first Swing application
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As a first Swing application, we’ll start with a window containing a single
button. To program with Swing, you need to import various classes from
Scala’s Swing API package:
import scala.swing._
Listing 34.1 shows the code of your first Swing application in Scala. If you
compile and run that file, you should see a window as shown on the left of
Figure 34.1. The window can be resized to a larger size as shown on the right
of Figure 34.1.
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Section 34.1
Chapter 34 · GUI Programming
Figure 34.1 · A simple Swing application: initial (left) and resized (right).
import scala.swing._
object FirstSwingApp extends SimpleSwingApplication {
def top = new MainFrame {
title = "First Swing App"
contents = new Button {
text = "Click me"
}
}
}
Listing 34.1 · A simple Swing application in Scala.
If you analyze the code in Listing 34.1 line by line, you’ll notice the
following elements:
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object FirstSwingApp extends SimpleSwingApplication {
In the first line after the import, the FirstSwingApp object inherits from
scala.swing.SimpleSwingApplication. This application differs from
traditional command-line applications, which may inherit from scala.App.
The SimpleSwingApplication class already defines a main method that
contains some setup code for Java’s Swing framework. The main method
then proceeds to call the top method, which you supply:
def top = new MainFrame {
The next line implements the top method. This method contains the
code that defines your top-level GUI component. This is usually some kind
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of Frame—i.e., a window that can contain arbitrary data. In Listing 34.1,
we chose a MainFrame as the top-level component. A MainFrame is like a
normal Swing Frame except that closing it will also close the whole GUI
application.
title = "First Swing App"
Frames have a number of attributes. Two of the most important are the
frame’s title, which will be written in the title bar, and its contents, which
will be displayed in the window itself. In Scala’s Swing API, such attributes
are modeled as properties. You know from Section 18.2 that properties are
encoded in Scala as pairs of getter and setter methods. For instance, the
title property of a Frame object is modeled as a getter method:
def title: String
and a setter method:
def title_=(s: String)
It is this setter method that gets invoked by the above assignment to title.
The effect of the assignment is that the chosen title is shown in the header of
the window. If you leave it out, the window will have an empty title.
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contents = new Button {
The top frame is the root component of the Swing application. It is a
Container, which means that further components can be defined in it. Every Swing container has a contents property, which allows you to get and
set the components it contains. The getter contents of this property has type
Seq[Component], indicating that a component can in general have several
objects as its contents. Frames, however, always have just a single component as their contents. This component is set and potentially changed
using the setter contents_=. For example, in Listing 34.1 a single Button
constitutes the contents of the top frame.
text = "Click me"
The button also gets a title, in this case “Click me.”
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Figure 34.2 · A reactive Swing application: initial (left) after clicks (right).
import scala.swing._
object SecondSwingApp extends SimpleSwingApplication {
def top = new MainFrame {
title = "Second Swing App"
val button = new Button {
text = "Click me"
}
val label = new Label {
text = "No button clicks registered"
}
contents = new BoxPanel(Orientation.Vertical) {
contents += button
contents += label
border = Swing.EmptyBorder(30, 30, 10, 30)
}
}
}
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Listing 34.2 · Component assembly on a panel.
34.2
Panels and layouts
As next step, we’ll add some text as a second content element to the top
frame of the application. The left part of Figure 34.2 shows what the application should look like.
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ponent. Hence, to make a frame with both a button and a label, you need to
create a different container component that holds both. That’s what panels
are used for. A Panel is a container that displays all the components it contains according to some fixed layout rules. There are a number of different
possible layouts that are implemented by various subclasses of class Panel,
ranging from simple to quite intricate. In fact, one of the hardest parts of a
complex GUI application can be getting the layouts right—it’s not easy to
come up with something that displays reasonably well on all sorts of devices
and for all window sizes.
Listing 34.2 shows a complete implementation. In this class, the two subcomponents of the top frame are named button and label. The button is
defined as before. The label is a displayed text field that can’t be edited:
val label = new Label {
text = "No button clicks registered"
}
The code in Listing 34.2 picks a simple vertical layout where components
are stacked on top of each other in a BoxPanel:
contents = new BoxPanel(Orientation.Vertical) {
The contents property of the BoxPanel is an (initially empty) buffer, to
which the button and label elements are added with the += operator:
contents += button
contents += label
We also add a border around the two objects by assigning to the border
property of the panel:
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border = Swing.EmptyBorder(30, 30, 10, 30)
As is the case with other GUI components, borders are represented as objects. EmptyBorder is a factory method in object Swing that takes four parameters indicating the width of the borders on the top, right, bottom, and
left sides of the objects to be drawn.
Simple as it is, the example has already shown the basic way to structure a GUI application. It is built from components, which are instances of
scala.swing classes such as Frame, Panel, Label or Button. Components
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have properties, which can be customized by the application. Panel components can contain several other components in their contents property, so
that in the end a GUI application consists of a tree of components.
34.3
Handling events
On the other hand, the application still misses an essential property. If you
run the code in Listing 34.2 and click on the displayed button, nothing happens. In fact, the application is completely static; it does not react in any
way to user events except for the close button of the top frame, which terminates the application. So as a next step, we’ll refine the application so that it
displays together with the button a label that indicates how often the button
was clicked. The right part of Figure 34.2 contains a snapshot of what the
application should look like after a few button clicks.
To achieve this behavior, you need to connect a user-input event (the button was clicked) with an action (the displayed label is updated). Java and
Scala have fundamentally the same "publish/subscribe" approach to event
handling: Components may be publishers and/or subscribers. A publisher
publishes events. A subscriber subscribes with a publisher to be notified of
any published events. Publishers are also called “event sources,” and subscribers are also called “event listeners”. For instance a Button is an event
source, which publishes an event, ButtonClicked, indicating that the button
was clicked.
In Scala, subscribing to an event source source is done by the call
listenTo(source). There’s also a way to unsubscribe from an event source
using deafTo(source). In the current example application, the first thing
to do is to get the top frame to listen to its button, so that it gets notified of
any events that the button issues. To do that you need to add the following
call to the body of the top frame:
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listenTo(button)
Being notified of events is only half the story; the other half is handling them.
It is here that the Scala Swing framework is most different from (and radically simpler than) the Java Swing API’s. In Java, signaling an event means
calling a “notify” method in an object that has to implement some Listener
interfaces. Usually, this involves a fair amount of indirection and boilerplate code, which makes event-handling applications hard to write and read.
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By contrast, in Scala, an event is a real object that gets sent to subscribing
components much like messages are sent to actors. For instance, pressing a
button will create an event which is an instance of the following case class:
case class ButtonClicked(source: Button)
The parameter of the case class refers to the button that was clicked. As
with all other Scala Swing events, this event class is contained in a package
named scala.swing.event.
To have your component react to incoming events you need to add a
handler to a property called reactions. Here’s the code that does this:
var nClicks = 0
reactions += {
case ButtonClicked(b) =>
nClicks += 1
label.text = "Number of button clicks: " + nClicks
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}
The first line above defines a variable, nClicks, which holds the number
of times a button was clicked. The remaining lines add the code between
braces as a handler to the reactions property of the top frame. Handlers
are functions defined by pattern matching on events, much like Akka actor
receive methods are defined by pattern matching on messages. The handler
above matches events of the form ButtonClicked(b), i.e., any event which
is an instance of class ButtonClicked. The pattern variable b refers to the
actual button that was clicked. The action that corresponds to this event in
the code above increments nClicks and updates the text of the label.
Generally, a handler is a PartialFunction that matches on events and
performs some actions. It is also possible to match on more than one kind of
event in a single handler by using multiple cases.
The reactions property implements a collection, just like the contents
property does. Some components come with predefined reactions. For instance, a Frame has a predefined reaction that it will close if the user presses
the close button on the upper right. If you install your own reactions by
adding them with += to the reactions property, the reactions you define
will be considered in addition to the standard ones. Conceptually, the handlers installed in reactions form a stack. In the current example, if the top
frame receives an event, the first handler tried will be the one that matches
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import scala.swing._
import scala.swing.event._
object ReactiveSwingApp extends SimpleSwingApplication {
def top = new MainFrame {
title = "Reactive Swing App"
val button = new Button {
text = "Click me"
}
val label = new Label {
text = "No button clicks registered"
}
contents = new BoxPanel(Orientation.Vertical) {
contents += button
contents += label
border = Swing.EmptyBorder(30, 30, 10, 30)
}
listenTo(button)
var nClicks = 0
reactions += {
case ButtonClicked(b) =>
nClicks += 1
label.text = "Number of button clicks: " + nClicks
}
}
}
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Listing 34.3 · Implementing a reactive Swing application.
on ButtonClicked, because it was the last handler installed for the frame.
If the received event is of type ButtonClicked, the code associated with
the pattern will be invoked. After that code has completed, the system will
search for further handlers in the event stack that might also be applicable.
If the received event is not of type ButtonClicked, the event is immediately propagated to the rest of the installed handler stack. It’s also possible
to remove handlers from the reaction property, using the -= operator.
Listing 34.3 shows the completed application, including reactions. The
code illustrates the essential elements of a GUI application in Scala’s Swing
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Figure 34.3 · A converter between degrees Celsius and Fahrenheit.
framework: The application consists of a tree of components, starting with
the top frame. The components shown in the code are Frame, BoxPanel,
Button, and Label, but there are many other kinds of components defined
in the Swing libraries. Each component is customized by setting attributes.
Two important attributes are contents, which fixes the children of a component in the tree, and reactions, which determines how the component
reacts to events.
34.4
Example: Celsius/Fahrenheit converter
As another example, we’ll write a GUI program that converts between temperature degrees in Celsius and Fahrenheit. The user interface of the program
is shown in Figure 34.3. It consists of two text fields (shown in white) with a
label following each. One text field shows temperatures in degrees Celsius,
the other in degrees Fahrenheit. Each of the two fields can be edited by the
user of the application. Once the user has changed the temperature in either
field, the temperature in the other field should automatically update.
Listing 34.4 shows the complete code that implements this application.
The imports at the top of the code use a short-hand:
import swing._
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import event._
This is in fact equivalent to the imports used before:
import scala.swing._
import scala.swing.event._
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774
import swing._
import event._
object TempConverter extends SimpleSwingApplication {
def top = new MainFrame {
title = "Celsius/Fahrenheit Converter"
object celsius extends TextField { columns = 5 }
object fahrenheit extends TextField { columns = 5 }
contents = new FlowPanel {
contents += celsius
contents += new Label(" Celsius
=
")
contents += fahrenheit
contents += new Label(" Fahrenheit")
border = Swing.EmptyBorder(15, 10, 10, 10)
}
listenTo(celsius, fahrenheit)
reactions += {
case EditDone(`fahrenheit`) =>
val f = fahrenheit.text.toInt
val c = (f - 32) * 5 / 9
celsius.text = c.toString
case EditDone(`celsius`) =>
val c = celsius.text.toInt
val f = c * 9 / 5 + 32
fahrenheit.text = f.toString
}
}
}
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Listing 34.4 · An implementation of the temperature converter.
The reason you can use the shorthand is that packages nest in Scala. Because package scala.swing is contained in package scala, and everything
in that package imported automatically, you can write just swing to refer
to the package. Likewise, package scala.swing.event, is contained as
subpackage event in package scala.swing. Because you have imported
everything in scala.swing in the first import, you can refer to the event
package with just event thereafter.
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Chapter 34 · GUI Programming
The two components celsius and fahrenheit in TempConverter are
objects of class TextField. A TextField in Swing is a component that lets
you edit a single line of text. It has a default width, which is given in the
columns property measured in characters (set to 5 for both fields).
The contents of TempConverter are assembled into a panel, which
includes the two text fields and two labels that explain what the fields are.
The panel is of class FlowPanel, which means it displays all its elements
one after another, in one or more rows, depending on the width of the frame.
The reactions of TempConverter are defined by a handler that contains two cases. Each case matches an EditDone event for one of the two
text fields. Such an event gets issued when a text field has been edited by
the user. Note the form of the patterns, which include back ticks around the
element names:
case EditDone(`celsius`)
As was explained in Section 15.2, the back ticks around celsius ensure that
the pattern matches only if the source of the event was the celsius object. If
you had omitted the back ticks and just written case EditDone(celsius),
the pattern would have matched every event of class EditDone. The changed
field would then be stored in the pattern variable celsius. Obviously, this is
not what you want. Alternatively, you could have defined the two TextField
objects starting with upper case characters, i.e., Celsius and Fahrenheit.
In that case you could have matched them directly without back ticks, as in
case EditDone(Celsius).
The two actions of the EditDone events convert one quantity to another.
Each starts by reading out the contents of the modified field and converting it
to an Int. It then applies the formula for converting one temperature degree
to the other, and stores the result back as a string in the other text field.
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34.5
Conclusion
This chapter has given you a first taste of GUI programming, using Scala’s
wrappers for the Swing framework. It has shown how to assemble GUI components, how to customize their properties, and how to handle events. For
space reasons, we could discuss only a small number of simple components.
There are many more kinds of components. You can find out about them by
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consulting the Scala documentation of the package scala.swing. The next
section will develop an example of a more complicated Swing application.
There are also many tutorials on the original Java Swing framework, on
which the Scala wrapper is based.1 The Scala wrappers resemble the underlying Swing classes, but try to simplify concepts where possible and make
them more uniform. The simplification makes extensive use of the properties of the Scala language. For instance, Scala’s emulation of properties and
its operator overloading allow convenient property definitions using assignments and += operations. Its “everything is an object” philosophy makes
it possible to inherit the main method of a GUI application. The method
can thus be hidden from user applications, including the boilerplate code for
setting things up that comes with it. Finally, and most importantly, Scala’s
first-class functions and pattern matching make it possible to formulate event
handling as the reactions component property, which greatly simplifies life
for the application developer.
1 See,
for instance, The Java Tutorials. [Jav]
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Chapter 35
The SCells Spreadsheet
In the previous chapters you saw many different constructs of the Scala programming language. In this chapter you’ll see how these constructs play
together in the implementation of a sizable application. The task is to write
a spreadsheet application, which will be named SCells.
There are several reasons why this task is interesting. First, everybody
knows spreadsheets, so it is easy to understand what the application should
do. Second, spreadsheets are programs that exercise a large range of different computing tasks. There’s the visual aspect, where a spreadsheet is seen
as a rich GUI application. There’s the symbolic aspect, having to do with
formulas and how to parse and interpret them. There’s the calculational aspect, dealing with how to update possibly large tables incrementally. There’s
the reactive aspect, where spreadsheets are seen as programs that react in
intricate ways to events. Finally, there’s the component aspect where the application is constructed as a set of reusable components. All these aspects
will be treated in depth in this chapter.
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35.1
The visual framework
We’ll start by writing the basic visual framework of the application. Figure 35.1 shows the first iteration of the user interface. You can see that a
spreadsheet is a scrollable table. It has rows going from 0 to 99 and columns
going from A to Z. You express this in Swing by defining a spreadsheet as a
ScrollPane containing a Table. Listing 35.1 shows the code.
The spreadsheet component shown in Listing 35.1 is defined in package org.stairwaybook.scells, which will contain all classes, traits, and
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Section 35.1
Chapter 35 · The SCells Spreadsheet
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Figure 35.1 · A simple spreadsheet table.
objects needed for the application. It imports from package scala.swing
essential elements of Scala’s Swing wrapper. Spreadsheet itself is a class
that takes height and width (in numbers of cells) as parameters. The class
extends ScrollPane, which gives it the scroll-bars at the bottom and right in
Figure 35.1. It contains two sub-components named table and rowHeader.
The table component is an instance of an anonymous subclass of class
scala.swing.Table. The four lines in its body set some of its attributes:
rowHeight for the height of a table row in points, autoResizeMode to turn
auto-sizing the table off, showGrid to show a grid of lines between cells, and
gridColor to set the color of the grid to a dark gray.
The rowHeader component, which contains the row-number headers at
the left of the spreadsheet in Figure 35.1, is a ListView that displays in its
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package org.stairwaybook.scells
import swing._
class Spreadsheet(val height: Int, val width: Int)
extends ScrollPane {
val table = new Table(height, width) {
rowHeight = 25
autoResizeMode = Table.AutoResizeMode.Off
showGrid = true
gridColor = new java.awt.Color(150, 150, 150)
}
val rowHeader =
new ListView((0 until height) map (_.toString)) {
fixedCellWidth = 30
fixedCellHeight = table.rowHeight
}
viewportView = table
rowHeaderView = rowHeader
}
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Listing 35.1 · Code for spreadsheet in Figure 35.1.
elements the strings 0 through 99. The two lines in its body fix the width of a
cell to be 30 points and the height to be the same as the table’s rowHeight.
The whole spreadsheet is assembled by setting two fields in ScrollPane.
The field viewportView is set to the table, and the field rowHeaderView
is set to the rowHeader list. The difference between the two views is that a
view port of a scroll pane is the area that scrolls with the two bars, whereas
the row header on the left stays fixed when you move the horizontal scroll
bar. By some quirk, Swing already supplies by default a column header at
the top of the table, so there’s no need to define one explicitly.
To try out the rudimentary spreadsheet shown in Listing 35.1, you just
need to define a main program that creates the Spreadsheet component.
Such a program is shown in Listing 35.2.
Program Main inherits from SimpleSwingApplication, which takes
care of all the low-level details that need to be set up before a Swing application can be run. You only need to define the top-level window of the appliCover · Overview · Contents · Discuss · Suggest · Glossary · Index
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package org.stairwaybook.scells
import swing._
object Main extends SimpleSwingApplication {
def top = new MainFrame {
title = "ScalaSheet"
contents = new Spreadsheet(100, 26)
}
}
Listing 35.2 · The main program for the spreadsheet application.
cation in the top method. In our example, top is a MainFrame that has two
elements defined: its title, set to “ScalaSheet,” and its contents, set to an
instance of class Spreadsheet with 100 rows and 26 columns. That’s all. If
you launch this application with scala org.stairwaybook.scells.Main,
you should see the spreadsheet in Figure 35.1.
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35.2
Disconnecting data entry and display
If you play a bit with the spreadsheet written so far, you’ll quickly notice that
the output that’s displayed in a cell is always exactly what you entered in the
cell. A real spreadsheet does not behave like that. In a real spreadsheet, you
would enter a formula and you’d see its value. So what is entered into a cell
is different from what is displayed.
As a first step to a real spreadsheet application, you should concentrate on disentangling data entry and display. The basic mechanism for
display is contained in the rendererComponent method of class Table.
By default, rendererComponent always displays what’s entered. If you
want to change that, you need to override rendererComponent to do something different. Listing 35.3 shows a new version of Spreadsheet with a
rendererComponent method.
The rendererComponent method overrides a default method in class
Table. It takes four parameters. The isSelected and hasFocus parameters
are Booleans that indicate whether the cell has been selected and whether it
has focus, meaning that keyboard events will go into the cell. The remaining
two parameters, row and column, give the cell’s coordinates.
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package org.stairwaybook.scells
import swing._
class Spreadsheet(val height: Int, val width: Int)
extends ScrollPane {
val cellModel = new Model(height, width)
import cellModel._
val table = new Table(height, width) {
// settings as before...
override def rendererComponent(isSelected: Boolean,
hasFocus: Boolean, row: Int, column: Int): Component =
if (hasFocus) new TextField(userData(row, column))
else
new Label(cells(row)(column).toString) {
xAlignment = Alignment.Right
}
def userData(row: Int, column: Int): String = {
val v = this(row, column)
if (v == null) "" else v.toString
}
}
// rest as before...
}
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Listing 35.3 · A spreadsheet with a rendererComponent method.
The new rendererComponent method checks whether the cell has input
focus. If hasFocus is true, the cell is used for editing. In this case you
want to display an editable TextField that contains the data the user has
entered so far. This data is returned by the helper method userData, which
displays the contents of the table at a given row and column. The contents
are retrieved by the call this(row, column).1 The userData method also
takes care to display a null element as the empty string instead of “null.”
1 Although “this(row, column)”
may look similar to a constructor invocation, it is in
this case an invocation of the apply method on the current Table instance.
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package org.stairwaybook.scells
class Model(val height: Int, val width: Int) {
case class Cell(row: Int, column: Int)
val cells = Array.ofDim[Cell](height, width)
for (i <- 0 until height; j <- 0 until width)
cells(i)(j) = new Cell(i, j)
}
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Listing 35.4 · First version of the Model class.
So far so good. But what should be displayed if the cell does not have
focus? In a real spreadsheet this would be the value of a cell. Thus, there
are really two tables at work. The first table, named table contains what the
user entered. A second “shadow” table contains the internal representation of
cells and what should be displayed. In the spreadsheet example, this table is a
two-dimensional array called cells. If a cell at a given row and column does
not have editing focus, the rendererComponent method will display the
element cells(row)(column). The element cannot be edited, so it should
be displayed in a Label instead of in an editable TextField.
It remains to define the internal array of cells. You could do this directly
in the Spreadsheet class, but it’s generally preferable to separate the view
of a GUI component from its internal model. That’s why in the example
above the cells array is defined in a separate class named Model. The
model is integrated into the Spreadsheet by defining a value cellModel
of type Model. The import clause that follows this val definition makes
the members of cellModel available inside Spreadsheet without having to
prefix them. Listing 35.4 shows a first simplified version of a Model class.
The class defines an inner class, Cell, and a two-dimensional array, cells,
of Cell elements. Each element is initialized to be a fresh Cell.
That’s it. If you compile the modified Spreadsheet class with the Model
class and run the Main application you should see a window as in Figure 35.2.
The objective of this section was to arrive at a design where the displayed
value of a cell is different from the string that was entered into it. This objective has clearly been met, albeit in a very crude way. In the new spreadsheet
you can enter anything you want into a cell, but it will always display just its
coordinates once it loses focus. Clearly, we are not done yet.
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Figure 35.2 · Cells displaying themselves.
35.3
Formulas
In reality, a spreadsheet cell holds two things: An actual value and a formula
to compute this value. There are three types of formulas in a spreadsheet:
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1. Numeric values such as 1.22, -3, or 0.
2. Textual labels such as Annual sales, Deprecation, or total.
3. Formulas that compute a new value from the contents of cells, such as
“=add(A1,B2)”, or “=sum(mul(2, A2), C1:D16)”
A formula that computes a value always starts with an equals sign and is
followed by an arithmetic expression. The SCells spreadsheet has a parCover · Overview · Contents · Discuss · Suggest · Glossary · Index
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ticularly simple and uniform convention for arithmetic expressions: every
expression is an application of some function to a list of arguments. The
function name is an identifier such as add for binary addition, or sum for
summation of an arbitrary number of operands. A function argument can
be a number, a reference to a cell, a reference to a range of cells such as
C1:D16, or another function application. You’ll see later that SCells has an
open architecture that makes it easy to install your own functions via mixin
composition.
The first step to handling formulas is writing down the types that represent them. As you might expect, the different kinds of formulas are represented by case classes. Listing 35.5 shows the contents of a file named
Formulas.scala, where these case classes are defined:
package org.stairwaybook.scells
trait Formula
case class Coord(row: Int, column: Int) extends Formula {
override def toString = ('A' + column).toChar.toString + row
}
case class Range(c1: Coord, c2: Coord) extends Formula {
override def toString = c1.toString + ":" + c2.toString
}
case class Number(value: Double) extends Formula {
override def toString = value.toString
}
case class Textual(value: String) extends Formula {
override def toString = value
}
case class Application(function: String,
arguments: List[Formula]) extends Formula {
override def toString =
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function + arguments.mkString("(", ",", ")")
}
object Empty extends Textual("")
Listing 35.5 · Classes representing formulas.
Trait Formula, shown in Listing 35.5, has five case classes as children:
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Coord
Range
Number
Textual
Application
for cell coordinates such as A3,
for cell ranges such as A3:B17,
for floating-point numbers such as 3.1415,
for textual labels such as Deprecation,
for function applications such as sum(A1,A2).
Each case class overrides the toString method so that it displays its kind of
formula in the standard way shown above. For convenience there’s also an
Empty object that represents the contents of an empty cell. The Empty object
is an instance of the Textual class with an empty string argument.
35.4
Parsing formulas
In the previous section you saw the different kinds of formulas and how they
display as strings. In this section you’ll see how to reverse the process: to
transform a user input string into a Formula tree. The rest of this section explains one by one the different elements of a class FormulaParsers, which
contains the parsers that do the transformation. The class builds on the combinator framework given in Chapter 33. Specifically, formula parsers are an
instance of the RegexParsers class explained in that chapter:
package org.stairwaybook.scells
import scala.util.parsing.combinator._
object FormulaParsers extends RegexParsers {
The first two elements of object FormulaParsers are auxiliary parsers
for identifiers and decimal numbers:
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def ident: Parser[String] = """[a-zA-Z_]\w*""".r
def decimal: Parser[String] = """-?\d+(\.\d*)?""".r
As you can see from the first regular expression above, an identifier starts
with a letter or underscore. This is followed by an arbitrary number of
“word” characters represented by the regular expression code \w, which recognizes letters, digits or underscores. The second regular expression describes decimal numbers, which consist of an optional minus sign, one or
more digits that are represented by regular expression code \d, and an optional decimal part consisting of a period followed by zero or more digits.
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The next element of object FormulaParsers is the cell parser, which
recognizes the coordinates of a cell, such as C11 or B2. It first calls a regular
expression parser that determines the form of a coordinate: a single letter
followed by one or more digits. The string returned from that parser is then
converted to a cell coordinate by separating the letter from the numerical part
and converting the two parts to indices for the cell’s column and row:
def cell: Parser[Coord] =
"""[A-Za-z]\d+""".r ˆˆ { s =>
val column = s.charAt(0).toUpper - 'A'
val row = s.substring(1).toInt
Coord(row, column)
}
Note that the cell parser is a bit restrictive in that it allows only column
coordinates consisting of a single letter. Hence the number of spreadsheet
columns is in effect restricted to be at most 26, because further columns
cannot be parsed. It’s a good idea to generalize the parser so that it accepts
cells with several leading letters. This is left as an exercise to you.
The range parser recognizes a range of cells. Such a range is composed
of two cell coordinates with a colon between them:
def range: Parser[Range] =
cell~":"~cell ˆˆ {
case c1~":"~c2 => Range(c1, c2)
}
The number parser recognizes a decimal number, which is converted to
a Double and wrapped in an instance of the Number class:
def number: Parser[Number] =
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decimal ˆˆ (d => Number(d.toDouble))
The application parser recognizes a function application. Such an application is composed of an identifier followed by a list of argument expressions in parentheses:
def application: Parser[Application] =
ident~"("~repsep(expr, ",")~")" ˆˆ {
case f~"("~ps~")" => Application(f, ps)
}
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The expr parser recognizes a formula expression—either a top-level formula following an ‘=’, or an argument to a function. Such a formula expression is defined to be a cell, a range of cells, a number, or an application:
def expr: Parser[Formula] =
range | cell | number | application
This definition of the expr parser contains a slight oversimplification because ranges of cells should only appear as function arguments; they should
not be allowed as top-level formulas. You could change the formula grammar so that the two uses of expressions are separated, and ranges are excluded syntactically from top-level formulas. In the spreadsheet presented
here such an error is instead detected once an expression is evaluated.
The textual parser recognizes an arbitrary input string, as long as it
does not start with an equals sign (recall that strings that start with ‘=’ are
considered to be formulas):
def textual: Parser[Textual] =
"""[ˆ=].*""".r ˆˆ Textual
The formula parser recognizes all kinds of legal inputs into a cell. A
formula is either a number, or a textual entry, or a formula starting with an
equals sign:
def formula: Parser[Formula] =
number | textual | "="~>expr
This concludes the grammar for spreadsheet cells. The final method
parse uses this grammar in a method that converts an input string into a
Formula tree:
def parse(input: String): Formula =
parseAll(formula, input) match {
case Success(e, _) => e
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case f: NoSuccess => Textual("[" + f.msg + "]")
}
} //end FormulaParsers
The parse method parses all of the input with the formula parser. If
that succeeds, the resulting formula is returned. If it fails, a Textual object
with an error message is returned instead.
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package org.stairwaybook.scells
import swing._
import event._
class Spreadsheet(val height: Int, val width: Int) ... {
val table = new Table(height, width) {
...
reactions += {
case TableUpdated(table, rows, column) =>
for (row <- rows)
cells(row)(column).formula =
FormulaParsers.parse(userData(row, column))
}
}
}
Listing 35.6 · A spreadsheet that parses formulas.
That’s everything there is to parsing formulas. The only thing that remains is to integrate the parser into the spreadsheet. To do this, you can
enrich the Cell class in class Model by a formula field:
case class Cell(row: Int, column: Int) {
var formula: Formula = Empty
override def toString = formula.toString
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}
In the new version of the Cell class, the toString method is defined to
display the cell’s formula. That way you can check whether formulas have
been correctly parsed.
The last step in this section is to integrate the parser into the spreadsheet.
Parsing a formula happens as a reaction to the user’s input into a cell. A completed cell input is modeled in the Swing library by a TableUpdated event.
The TableUpdated class is contained in package scala.swing.event. The
event is of the form:
TableUpdated(table, rows, column)
It contains the table that was changed, as well as a set of coordinates of
affected cells given by rows and column. The rows parameter is a range
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Figure 35.3 · Cells displaying their formulas.
value of type Range[Int].2 The column parameter is an integer. So in
general a TableUpdated event can refer to several affected cells, but they
would be on a consecutive range of rows and share the same column.
Once a table is changed, the affected cells need to be re-parsed. To react to a TableUpdated event, you add a case to the reactions value of
the table component, as is shown in Listing 35.6. Now, whenever the table is edited the formulas of all affected cells will be updated by parsing the
corresponding user data. When compiling the classes discussed so far and
launching the scells.Main application you should see a spreadsheet application like the one shown in Figure 35.3. You can edit cells by typing into
2 Range[Int]
is also the type of a Scala expression such as “1 to N”.
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them. After editing is done, a cell displays the formula it contains. You can
also try to type some illegal input such as the one reading =add(1, X) in
the field that has the editing focus in Figure 35.3. Illegal input will show
up as an error message. For instance, once you’d leave the edited field in
Figure 35.3 you should see the error message [`(' expected] in the cell (to
see all of the error message you might need to widen the column by dragging
the separation between the column headers to the right).
35.5
Evaluation
Of course, in the end a spreadsheet should evaluate formulas, not just display
them. In this section, we’ll add the necessary components to achieve this.
What’s needed is a method, evaluate, which takes a formula and returns the value of that formula in the current spreadsheet, represented as a
Double. We’ll place this method in a new trait, Evaluator. The method
needs to access the cells field in class Model to find out about the current
values of cells that are referenced in a formula. On the other hand, the Model
class needs to call evaluate. Hence, there’s a mutual dependency between
the Model and the Evaluator. A good way to express such mutual dependencies between classes was shown in Chapter 29: you use inheritance in
one direction and self types in the other.
In the spreadsheet example, class Model inherits from Evaluator and
thus gains access to its evaluation method. To go the other way, class
Evaluator defines its self type to be Model, like this:
package org.stairwaybook.scells
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trait Evaluator { this: Model => ...
That way, the this value inside class Evaluator is assumed to be Model
and the cells array is accessible by writing either cells or this.cells.
Now that the wiring is done, we’ll concentrate on defining the contents of
class Evaluator. Listing 35.7 shows the implementation of the evaluate
method. As you might expect, the method contains a pattern match over
the different types of formulas. For a coordinate Coord(row, column),
it returns the value of the cells array at that coordinate. For a number
Number(v), it returns the value v. For a textual label Textual(s), it returns
zero. Finally, for an application Application(function, arguments), it
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def evaluate(e: Formula): Double = try {
e match {
case Coord(row, column) =>
cells(row)(column).value
case Number(v) =>
v
case Textual(_) =>
0
case Application(function, arguments) =>
val argvals = arguments flatMap evalList
operations(function)(argvals)
}
} catch {
case ex: Exception => Double.NaN
}
Listing 35.7 · The evaluate method of trait Evaluator.
computes the values of all arguments, retrieves a function object corresponding to the function name from an operations table and applies that function to all argument values.
The operations table maps function names to function objects. It is
defined as follows:
type Op = List[Double] => Double
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val operations = new collection.mutable.HashMap[String, Op]
As you can see from this definition, operations are modeled as functions
from lists of values to values. The Op type introduces a convenient alias for
the type of an operation.
The computation in evaluate is wrapped in a try-catch to guard against
input errors. There are actually quite a few things that can go wrong when
evaluating a cell formula: coordinates might be out of range; function names
might be undefined; functions might have the wrong number of arguments;
arithmetic operations might be illegal or overflow. The reaction to any of
these errors is the same: a “not-a-number” value is returned. The returned
value, Double.NaN, is the IEEE representation for a computation that does
not have a representable floating-point value. This might happen because
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of an overflow or a division by zero, for example. The evaluate method
of Listing 35.7 chooses to return the same value also for all other kinds of
errors. The advantage of this scheme is that it’s simple to understand and
doesn’t require much code to implement. Its disadvantage is that all kinds of
errors are lumped together, so a spreadsheet user does not get any detailed
feedback on what went wrong. If you wish you can experiment with more
refined ways of representing errors in the SCells application.
The evaluation of arguments is different from the evaluation of top-level
formulas. Arguments may be lists whereas top-level functions may not. For
instance, the argument expression A1:A3 in sum(A1:A3) returns the values
of cells A1, A2, A3 in a list. This list is then passed to the sum operation. It’s
also possible to mix lists and single values in argument expressions, for instance the operation sum(A1:A3, 1.0, C7), which would sum up five values.
To handle arguments that might evaluate to lists, there’s another evaluation
function, called evalList, which takes a formula and returns a list of values:
private def evalList(e: Formula): List[Double] = e match {
case Range(_, _) => references(e) map (_.value)
case _ => List(evaluate(e))
}
If the formula argument passed to evalList is a Range, the returned value is
a list consisting of the values of all cells referenced by the range. For every
other formula the result is a list consisting of the single result value of that
formula. The cells referenced by a formula are computed by a third function,
references. Here is its definition:
def references(e: Formula): List[Cell] = e match {
case Coord(row, column) =>
List(cells(row)(column))
case Range(Coord(r1, c1), Coord(r2, c2)) =>
for (row <- (r1 to r2).toList; column <- c1 to c2)
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yield cells(row)(column)
case Application(function, arguments) =>
arguments flatMap references
case _ =>
List()
}
} // end Evaluator
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The references method is actually more general than needed right now
in that it computes the list of cells referenced by any sort of formula, not
just a Range formula. It will turn out later that the added functionality is
needed to compute the sets of cells that need updating. The body of the
method is a straightforward pattern match on kinds of formulas. For a coordinate Coord(row, column), it returns a single-element list containing the
cell at that coordinate. For a range expression Range(coord1, coord2), it
returns all cells between the two coordinates, computed by a for expression.
For a function application Application(function, arguments), it returns
the cells referenced by each argument expression, concatenated via flatMap
into a single list. For the other two types of formulas, Textual and Number,
it returns an empty list.
35.6
Operation libraries
The class Evaluator itself defines no operations that can be performed on
cells: its operations table is initially empty. The idea is to define such operations in other traits, which are then mixed into the Model class. Listing 35.8
shows an example trait that implements common arithmetic operations:
package org.stairwaybook.scells
trait Arithmetic { this: Evaluator =>
operations += (
"add"
-> { case List(x, y) => x + y },
"sub"
-> { case List(x, y) => x - y },
"div"
-> { case List(x, y) => x / y },
"mul"
"mod"
-> { case List(x, y) => x * y },
-> { case List(x, y) => x % y },
"sum"
-> { xs => (0.0 /: xs)(_ + _) },
"prod" -> { xs => (1.0 /: xs)(_ * _) }
)
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}
Listing 35.8 · A library for arithmetic operations.
Interestingly, this trait has no exported members. The only thing it does
is populate the operations table during its initialization. It gets access to
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Section 35.6
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that table by using a self type Evaluator, i.e., by the same technique the
Arithmetic class uses to get access to the model.
Of the seven operations that are defined by the Arithmetic trait, five are
binary operations and two take an arbitrary number of arguments. The binary
operations all follow the same schema. For instance, the addition operation
add is defined by the expression:
{ case List(x, y) => x + y }
That is, it expects an argument list consisting of two elements x and y and
returns the sum of x and y. If the argument list contains a number of elements
different from two, a MatchError is thrown. This corresponds to the general
“let it crash” philosophy of SCell’s evaluation model, where incorrect input
is expected to lead to a runtime exception that then gets caught by the trycatch inside the evaluation method.
The last two operations, sum and prod, take a list of arguments of arbitrary length and insert a binary operation between successive elements. So
they are instances of the “fold left” schema that’s expressed in class List by
the /: operation. For instance, to sum a list of numbers List(x, y, z), the
operation computes 0 + x + y + z. The first operand, 0, is the result if the list
is empty.
You can integrate this operation library into the spreadsheet application
by mixing the Arithmetic trait into the Model class, like this:
package org.stairwaybook.scells
class Model(val height: Int, val width: Int)
extends Evaluator with Arithmetic {
case class Cell(row: Int, column: Int) {
var formula: Formula = Empty
def value = evaluate(formula)
override def toString = formula match {
Prepared for Library Genesis
case Textual(s) => s
case _ => value.toString
}
}
... // rest as before
}
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Figure 35.4 · Cells that evaluate.
Another change in the Model class concerns the way cells display themselves. In the new version, the displayed value of a cell depends on its formula. If the formula is a Textual field, the contents of the field are displayed
literally. In all other cases, the formula is evaluated and the result value of
that evaluation is displayed.
If you compile the changed traits and classes and relaunch the Main
program you get something that starts to resemble a real spreadsheet. Figure 35.4 shows an example. You can enter formulas into cells and get them
to evaluate themselves. For instance, once you close the editing focus on cell
C5 in Figure 35.4, you should see 86.0, the result of evaluating the formula
sum(C1:C4).
However, there’s a crucial element still missing. If you change the value
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Section 35.7
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of cell C1 in Figure 35.4 from 20 to 100, the sum in cell C5 will not be
automatically updated to 166. You’ll have to click on C5 manually to see a
change in its value. What’s still missing is a way to have cells recompute
their values automatically after a change.
35.7
Change propagation
If a cell’s value has changed, all cells that depend on that value should have
their results recomputed and redisplayed. The simplest way to achieve this
would be to recompute the value of every cell in the spreadsheet after each
change. However such an approach does not scale well as the spreadsheet
grows in size.
A better approach is to recompute the values of only those cells that refer to a changed cell in their formula. The idea is to use an event-based
publish/subscribe framework for change propagation: once a cell gets assigned a formula, it will subscribe to be notified of all value changes in cells
to which the formula refers. A value change in one of these cells will trigger
a re-evaluation of the subscriber cell. If such a re-evaluation causes a change
in the value of the cell, it will in turn notify all cells that depend on it. The
process continues until all cell values have stabilized, i.e., there are no more
changes in the values of any cell.3
The publish/subscribe framework is implemented in class Model using
the standard event mechanism of Scala’s Swing framework. Here’s a new
(and final) version of this class:
package org.stairwaybook.scells
import swing._
class Model(val height: Int, val width: Int)
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extends Evaluator with Arithmetic {
Compared to the previous version of Model, this version adds a new import
of swing._, which makes Swing’s event abstractions directly available.
The main modifications of class Model concern the nested class Cell.
Class Cell now inherits from Publisher, so that it can publish events. The
event-handling logic is completely contained in the setters of two properties:
value and formula. Here is Cell’s new version:
3 This
assumes that there are no cyclic dependencies between cells. We discuss dropping
this assumption at the end of this chapter.
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case class Cell(row: Int, column: Int) extends Publisher {
override def toString = formula match {
case Textual(s) => s
case _ => value.toString
}
To the outside, it looks like value and formula are two variables in class
Cell. Their actual implementation is in terms of two private fields that are
equipped with public getters, value and formula, and setters, value_= and
formula_=. Here are the definitions implementing the value property:
private var v: Double = 0
def value: Double = v
def value_=(w: Double) = {
if (!(v == w || v.isNaN && w.isNaN)) {
v = w
publish(ValueChanged(this))
}
}
The value_= setter assigns a new value w to the private field v. If the new
value is different from the old one, it also publishes a ValueChanged event
with the cell itself as argument. Note that the test whether the value has
changed is a bit tricky because it involves the value NaN. The Java spec says
that NaN is different from every other value, including itself! Therefore, a
test whether two values are the same has to treat NaN specially: two values v,
w are the same if they are equal with respect to ==, or they are both the value
NaN, i.e., v.isNaN and w.isNaN both yield true.
Whereas the value_= setter does the publishing in the publish/subscribe
framework, the formula_= setter does the subscribing:
private var f: Formula = Empty
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def formula: Formula = f
def formula_=(f: Formula) = {
for (c <- references(formula)) deafTo(c)
this.f = f
for (c <- references(formula)) listenTo(c)
value = evaluate(f)
}
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If a cell is assigned a new formula, it first unsubscribes with deafTo from
all cells referenced by the previous formula value. It then stores the new
formula in the private variable f and subscribes with listenTo to all cells
referenced by it. Finally, it recomputes its value using the new formula.
The last piece of code in the revised class Cell specifies how to react to
a ValueChanged event:
reactions += {
case ValueChanged(_) => value = evaluate(formula)
}
} // end class Cell
The ValueChanged class is also contained in class Model:
case class ValueChanged(cell: Cell) extends event.Event
The rest of class Model is as before:
val cells = Array.ofDim[Cell](height, width)
for (i <- 0 until height; j <- 0 until width)
cells(i)(j) = new Cell(i, j)
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} // end class Model
The spreadsheet code is now almost complete. The final piece missing
is the re-display of modified cells. So far, all value propagation concerned
the internal Cell values only; the visible table was not affected. One way to
change this would be to add a redraw command to the value_= setter. However, this would undermine the strict separation between model and view that
you have seen so far. A more modular solution is to notify the table of all
ValueChanged events and let it do the redrawing itself. Listing 35.9 shows
the final spreadsheet component, which implements this scheme.
Class Spreadsheet of Listing 35.9 has only two new revisions. First, the
table component now subscribes with listenTo to all cells in the model.
Second, there’s a new case in the table’s reactions: if it is notified of a
ValueChanged(cell) event, it demands a redraw of the corresponding cell
with a call of updateCell(cell.row, cell.column).
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Section 35.7
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package org.stairwaybook.scells
import swing._, event._
class Spreadsheet(val height: Int, val width: Int)
extends ScrollPane {
val cellModel = new Model(height, width)
import cellModel._
val table = new Table(height, width) {
... // settings as in Listing 35.1
override def rendererComponent(
isSelected: Boolean, hasFocus: Boolean,
row: Int, column: Int) =
... // as in Listing 35.3
def userData(row: Int, column: Int): String =
... // as in Listing 35.3
reactions += {
case TableUpdated(table, rows, column) =>
for (row <- rows)
cells(row)(column).formula =
FormulaParsers.parse(userData(row, column))
case ValueChanged(cell) =>
updateCell(cell.row, cell.column)
}
for (row <- cells; cell <- row) listenTo(cell)
}
val rowHeader = new ListView(0 until height) {
fixedCellWidth = 30
fixedCellHeight = table.rowHeight
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}
viewportView = table
rowHeaderView = rowHeader
}
Listing 35.9 · The finished spreadsheet component.
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35.8
Chapter 35 · The SCells Spreadsheet
Conclusion
The spreadsheet developed in this chapter is fully functional, even though
at some points it adopts the simplest solution to implement rather than the
most convenient one for the user. That way, it could be written in just under
200 lines of code. Nevertheless, the architecture of the spreadsheet makes
modifications and extensions easy. In case you would like to experiment with
the code a bit further, here are some suggestions of what you could change
or add:
1. You could make the spreadsheet resizable, so that the number of rows
and columns can be changed interactively.
2. You could add new kinds of formulas, for instance binary operations,
or other functions.
3. You might think about what to do when cells refer recursively to themselves. For instance, if cell A1 holds the formula add(B1, 1) and cell
B1 holds the formula mul(A1, 2), a re-evaluation of either cell will
trigger a stack-overflow. Clearly, that’s not a very good solution. As
alternatives, you could either disallow such a situation, or just compute
one iteration each time one of the cells is touched.
4. You could enhance error handling, giving more detailed messages describing what went wrong.
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5. You could add a formula entry field at the top of the spreadsheet, so
that long formulas could be entered more conveniently.
At the beginning of this book we stressed the scalability aspect of Scala.
We claimed that the combination of Scala’s object-oriented and functional
constructs makes it suitable for programs ranging from small scripts to very
large systems. The spreadsheet presented here is clearly still a small system,
even though it would probably take up much more than 200 lines in most
other languages. Nevertheless, you can see many of the details that make
Scala scalable at play in this application.
The spreadsheet uses Scala’s classes and traits with their mixin composition to combine its components in flexible ways. Recursive dependencies
between components are expressed using self types. The need for static state
is completely eliminated—the only top-level components that are not classes
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are formula trees and formula parsers, and both of these are purely functional. The application also uses higher-order functions and pattern matching extensively, both for accessing formulas and for event handling. So it is
a good showcase of how functional and object-oriented programming can be
combined smoothly.
One important reason why the spreadsheet application is so concise is
that it can base itself on powerful libraries. The parser combinator library
provides in effect an internal domain-specific language for writing parsers.
Without it, parsing formulas would have been much more difficult. The event
handling in Scala’s Swing libraries is a good example of the power of control
abstractions. If you know Java’s Swing libraries, you probably appreciate the
conciseness of Scala’s reactions concept, particularly when compared to the
tedium of writing notify methods and implementing listener interfaces in the
classical publish/subscribe design pattern. So the spreadsheet demonstrates
the benefits of extensibility, where high-level libraries can be made to look
just like language extensions.
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801
Appendix A
Scala Scripts on Unix and Windows
If you’re on some flavor of Unix, you can run a Scala script as a shell script
by prepending a “pound bang” directive at the top of the file. For example,
type the following into a file named helloarg:
#!/bin/sh
exec scala "$0" "$@"
!#
// Say hello to the first argument
println("Hello, " + args(0) + "!")
The initial #!/bin/sh must be the very first line in the file. Once you set its
execute permission:
$ chmod +x helloarg
You can run the Scala script as a shell script by simply saying:
$ ./helloarg globe
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If you’re on Windows, you can achieve the same effect by naming the
file helloarg.bat and placing this at the top of your script:
::#!
@echo off
call scala %0 %*
goto :eof
::!#
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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.
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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.
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Appendix A · Scala Scripts on Unix and Windows
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.
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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. Byvalue 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 Array[String](2)”, the class being instantiated
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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].
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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
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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.
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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,
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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.
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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
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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.
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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.
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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.
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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.
message Actors communicate with each other by sending each other messages. Sending a message does not interrupt what the receiver is doing. The receiver can wait until it has finished its current activity and
its invariants have been reestablished.
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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.
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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
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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.
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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.
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reassignable A variable may or may not be reassignable. A var is reassignable while a val is not.
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.
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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
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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.
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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 29.
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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.
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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
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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.
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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
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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.
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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 identity[T](x:T) = 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.
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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.
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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).
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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.
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yield An expression can yield a result. The yield keyword designates the
result of a for expression.
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[Eck98] Eckel, Bruce. Thinking in Java. Prentice Hall, 1998.
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[Fow04] Fowler, Martin. “Inversion of Control Containers and the Dependency Injection pattern.” January 2004. Available on the web at
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(accesssed June 6, 2008).
[Lan66] Landin, Peter J. “The Next 700 Programming Languages.” Communications of the ACM, 9(3):157–166, 1966.
[Mey91] Meyers, Scott. Effective C++. Addison-Wesley, 1991.
Prepared for Library Genesis
[Mey00] Meyer, Bertrand. Object-Oriented Software Construction. Prentice
Hall, 2000.
[Mor68] Morrison, Donald R. “PATRICIA—Practical Algorithm To Retrieve Information Coded in Alphanumeric.” J. ACM, 15(4):514–
534, 1968. ISSN 0004-5411. doi:http://doi.acm.org/10.1145/
321479.321481.
[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.
Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
820
Bibliography
[Ode05] Odersky, Martin and Matthias Zenger. “Scalable Component Abstractions.” In Proceedings of OOPSLA, pages 41–58. October
2005.
[Ode11] Odersky, Martin. The Scala Language Specification, Version 2.9.
EPFL, May 2011. Available on the web at http://www.scalalang.org/docu/manuals.html (accessed April 20, 2014).
[Ray99] Raymond, Eric. The Cathedral & the Bazaar: Musings on Linux
and Open Source by an Accidental Revolutionary. O’Reilly, 1999.
[Rum04] Rumbaugh, James, Ivar Jacobson, and Grady Booch. The Unified Modeling Language Reference Manual (2nd Edition). AddisonWesley, 2004.
[SPJ02] Simon Peyton Jones, et.al.
“Haskell 98 Language
and Libraries, Revised Report.”
Technical report,
http://www.haskell.org/onlinereport, 2002.
[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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[Vaz07] Vaziri, Mandana, Frank Tip, Stephen Fink, and Julian Dolby.
“Declarative Object Identity Using Relation Types.” In Proc.
ECOOP 2007, pages 54–78. 2007.
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About the Authors
Martin Odersky is the creator of the Scala language. He is a professor at
EPFL in Lausanne, Switzerland, and a founder of Typesafe, Inc. He 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 Semmle, Ltd. He worked on Scala
for two years as a post-doc at EPFL. He has a Ph.D. from Georgia Tech,
where he worked on static analysis of dynamic languages. 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 that powers Semmle. He and his wife live in Atlanta with
two cats and a chihuahua.
Bill Venners is president of Artima, Inc., publisher of the Artima Developer website (www.artima.com), and cofounder of Escalate Software, LLC.
He is author of the book, Inside the Java Virtual Machine, a programmeroriented survey of the Java platform’s architecture and internals. His popular
columns in JavaWorld magazine covered Java internals, object-oriented design, and Jini. Active in the Jini Community since its inception, Bill led the
Jini Community’s ServiceUI project, whose ServiceUI API became the de
facto standard way to associate user interfaces to Jini services. Bill is also
the lead developer and designer of ScalaTest, an open source testing tool for
Scala and Java developers.
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Index
on Map traits, 382, 553
on Set traits, 380, 548
on trait Iterator, 587
on Traversable traits, 535
++= (add elements to)
on trait mutable.Map, 383, 554
on trait mutable.Set, 380, 549
++= (append)
on trait Buffer, 547
++=: (prepend)
on trait Buffer, 547
+: (prepend)
on Seq traits, 544
+= (add element to)
on trait mutable.Map, 383, 554
on trait mutable.Set, 380, 549
+= (add to)
on trait mutable.Set, 88
reassignment with immutable
sets and vars, 89
+= (append)
on class ArrayBuffer, 376
on class ListBuffer, 375, 506
on trait Buffer, 547
+=: (prepend)
on class ListBuffer, 375
on trait Buffer, 547
- (contravariant in)
variance annotation, 426
- (minus)
on numeric types, 124
- (remove element)
Page numbers followed by an n refer
to footnotes.
Symbols
! (invert)
Prepared for Library Genesis
on class Boolean, 125
! (message send)
on trait Actor, 49
!= (not equals)
method on Any, 128, 246
" (string delimiter), 117
""" (raw string delimiter), 117
’ (character delimiter), 116
() (empty parentheses)
on method definitions, 221
shorthand for apply method
invocation, 79
() (unit value), 158
* (repeat)
on class String, 97
(repeated
parameter), 195
*
(times)
*
on numeric types, 124
+ (add element)
on Map traits, 382, 553
on Set traits, 88, 380, 548
+ (covariant in)
List’s type parameter, 500
variance annotation, 426
+ (plus)
on numeric types, 124
++ (add elements)
823
Prepared for Library Genesis
Index
on Map traits, 382, 553
on Set traits, 380, 549
-- (remove elements)
on Map traits, 382, 553
on Set traits, 380, 549
--= (remove elements from)
on trait mutable.Map, 383, 554
on trait mutable.Set, 380, 550
-= (remove element from)
on trait mutable.Map, 383, 554
on trait mutable.Set, 380, 549
on trait Buffer, 547
-> (map to), 91
in implicit conversion, 484
/ (divided by)
on numeric types, 124
/: (fold left)
on class List, 361
on trait Iterator, 590
on Traversable traits, 537
: (colon)
in type annotations, 66
:+ (append)
on Seq traits, 544
:: (cons) class
as pattern constructor, 344
subclass of List, 499–502
:: (cons) method
on class List, 82, 341
right associativity of, 83, 342
::: (concatenation)
on class List, 82, 345, 501
:\ (fold right)
on class List, 362
on trait Iterator, 590
on Traversable traits, 537
; (semicolon)
inference, 104
< (less than)
on numeric types, 125
<- (in)
in for expressions, 75
824
<: (upper bound), 441
<< (shift left)
on integral types, 128
<= (less than or equal to)
on numeric types, 125
= (variable assignment), 66
== (equals)
in set membership, 379
== (equals)
in Scala versus Java, 130
in-depth treatment, 674–697
method on Any, 128, 246, 674
quick introduction, 128–130
=> (right arrow, or “rocket”)
in function literals, 74
in import statements, 283–284
in self types, 669
in match alternatives, 170
in this aliasing, 753
> (greater than)
on numeric types, 125
>: (lower bound), 433
>= (greater than or equal to)
on numeric types, 125
>> (shift right)
on integral types, 128
>>> (unsigned shift right)
on integral types, 128
??? (unimplemented)
on object Predef, 347
@ (at sign)
in annotations, 638
in patterns, 320
to extract XML attributes, 652
# (pound sign)
in inner class type names, 459
% (remainder)
on numeric types, 124
& (bitwise-and)
on integral types, 127
& (intersect)
on Set traits, 380, 549
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Index
Prepared for Library Genesis
&& (logical-and)
on class Boolean, 126
&~ (diff)
on Set traits, 549
_ (underscore)
in curried functions, 211
in function literals, 187, 206
in identifiers, 148
in import statements, 284
in wildcard types, 692
in match expressions, 169
used to initialize a field to its
default value, 400
_* (underscore asterisk)
in XML pattern matching, 656
repeated parameter, 196
sequence wildcard pattern, 628
_1, _2, etc.(tuple accessors), 86
_= (underscore equals)
in setter method names, 398
^ (exclusive-or)
on integral types, 127
^^ (result conversion, or
“eyebrows”)
parser combinator, 745
~ (bitwise complement)
on integral types, 127
~ (sequential composition, or
“squiggle”)
parser combinator, 745, 754
~! (committed sequential
composition)
parser combinator, 762
~> (sequential composition right)
parser combinator, 746, 755
<~ (sequential composition left)
parser combinator, 746, 755
\ (extract)
on class Elem, 652
\\ (deep extract)
on class Elem, 652
| (alternative composition)
825
parser combinator, 746, 755
| (bitwise-or)
on integral types, 127
| (union)
on Set traits, 549
|| (logical-or)
on class Boolean, 126
{} (curly braces)
and variable scope, 174
in class definitions, 136
in imports, 283
in XML literals, 648–649
in XML patterns, 655
instead of parentheses in
method invocations, 213
instead of parentheses in for
expressions, 163
‘ (back tick)
in constants, 314
in identifiers, 149
A
Abelson, Harold, 401, 518
abs method
on class Double (via
RichDouble), 134
on class Int (via RichInt),
145, 249
abstract members, 443
abstract modifier
not needed on abstract methods,
fields, or types, 220, 443
on classes, 219
with override modifier on
members of traits, 268
abstract types, 455
access modifiers, 286–290
accessor methods, 398
Actor trait
methods
! (message send), 49
actors, 48, 49, 52, 328, 771
add method
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Index
Prepared for Library Genesis
on trait mutable.Set, 549
addString method
on class List, 353
on trait Iterator, 591
on Traversable traits, 538
Aho, Alfred, 518, 762n
Akka, 48, 50, 52, 328, 771
aliases
AnyRef for Object, 250
type, 444
aliasing
this, 753
object, see defensive copies
alphabetic versus symbolic names,
750
alternatives
catch-all, 311
definition of, 803
in match expressions, 169, 309
in catch clauses, 167
in partial function literals, 328
andThen method
on trait Future, 731
annotations
and reflection, 704
applied to expressions, 639
arguments to, 639
@BeanProperty, 643
@deprecated, 638, 641
in meta-programming, 638
Java-specific, 701
@native, 644
nested, 640
@serializable, 642, 702
@SerialVersionUID, 642
standard, 640
syntax of, 639
@tailrec, 643
@Test
in JUnit 4, 704
@throws, 702
@transient, 643
826
type, 46, 60, 66
for debugging, 371
@unchecked, 325, 409, 639,
644
used with named arguments,
640
variance, 426
@volatile, 642
anonymous classes, 448
anonymous functions, see function
literals
ANTLR, 736
Any class, 247
in Scala class hierarchy
diagram, 248
AnyRef class, 247
as alias of Object, 250
in Scala class hierarchy
diagram, 248
AnyVal class, 247
in Scala class hierarchy
diagram, 248
App trait, 110, 766
application
function to arguments, 188
partial function, 188, 356
applications, Scala, 108
apply method
called with parentheses, 79
in extractors, 623
on Array
class, 79
object, 81
on List
class, 351
object, 82, 365, 630
on Map
objects, 91
traits, 381, 382, 553
on object Future, 716
on Seq objects, 594
on Seq traits, 544
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Index
on Set objects, 87
on trait PartialFunction, 330
on FunctionN traits, 184
args variable
in scripts, 71
arguments
command line, in scripts, 71
named, 196
variable length, 195
versus parameters, 810
arithmetic operations (+, -, *, /, %)
on numeric types, 124–125
Array class, 374
ArrayBuffer class, 376
arrays, 236, 374
accessed with () not [], 79
converting to lists, 390
creating and initializing, 81
generic, 572
passing elements as arguments
with _*, 196
quick introduction, 78–81
asInstanceOf method
on class Any, 317
assert method
on object Predef, 94, 294
AssertionError class, 294
assertions, 294–296
assignment
result is unit value, 159
associativity, 132–133
right versus left, 83, 132
attributes
XML, 647, 652
automatic
boxing, 250
conversion, see implicit
conversion
imports, 285–286
auxiliary constructors, 142–143
B
backtracking, 761
827
base classes, see superclasses
BDD, see behavior-driven
development
BeanProperty annotation, 643,
662n
behavior-driven development, 299
BigInt class, 47
BigInteger class, 48
binary compatibility, xxxii, xxxiii,
709
Bison, 736
bitwise operations, on integral types
logical (&, |, ^, ~), 127
shift (<<, >>, >>>), 128
blank finals (Java)
not in Scala, 400
Bloch, Joshua, 675n, 688n, 695n
blocks, definition of, 804
Boolean class, 113, 125
boolean literals, 119
bottom types, 252
bound variables, 163, 191
boxing, of value types, 250
Bracha, Gilad, 518
break keyword (Java)
living without, 171
not in match expressions, 170
break method
on class Breaks, 173
breakable method
on class Breaks, 173
Breaks class, 173
buffer
members, 547
Buffer trait
methods
++=: (prepend), 547
++= (append), 547
+=: (prepend), 547
+= (append), 547
-= (remove element from),
547
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Index
clear, 547
clone, 547
insert, 547
insertAll, 547
remove, 547
trimEnd, 547
trimStart, 547
buffered method
on trait Iterator, 587
ButtonClicked class, 770
Prepared for Library Genesis
by-name parameters, 215, 407
by-value parameters, 756
Byte class, 112
C
C language, 53
parser generators, 736
C++ language, 53, 92
C# language, 92
enumerations, 461
properties, 400
static extension methods, 474
camel casing, 147
CanBuildFrom trait, 602
capitalization, of identifiers, 147
capitalize method
on class Int (via RichInt), 134
case classes, 307–308
versus extractors, 631–632
cases, see alternatives
casting, see also toInt, toChar, etc.
with asInstanceOf, 317
with typed patterns (the
preferred style), 317
catch clauses, 166
Cathedral and the Bazaar, The
(Raymond), 47
chained package clauses, 280
Char class, 112
character encoding
in XML, 654
character literals, 116
escape sequences, 117
828
checked exceptions, 167, 702
Church, Alonzo, 52
Class class, see classOf method
class hierarchy diagram
for lists, 500
for maps, 90
for Scala, 248
for sets, 88
for Try, 718
class parameters, 136
initialization order compared to
abstract fields, 449
not legal on singleton objects,
107
used to implement abstract
fields, 502
classes, 99–101
abstract, 219
annotating, 639
anonymous, 448
case, 307–308
companion, 105
generic, 418
implicit, 484
inner, see inner classes
nesting of, 240
private, 423
sealed, 323–325
virtual, 466
classOf method
on object Predef, 703
clear method
on trait mutable.Map, 555
on trait mutable.Set, 380, 550
on trait Buffer, 547
clone method
on trait mutable.Map, 555
on trait mutable.Set, 550
on trait Buffer, 547
closures, 191–195, 206
versus Java’s inner classes,
193n
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Prepared for Library Genesis
Index
code duplication
reducing, 58, 203–209
collect method
on trait Future, 723
collect method
on trait Iterator, 588
on Traversable traits, 535
collections, 373–394
api, 528
architecture, 597–620
conversion between Java and
Scala, 594–596
parallel, 46, 529
views of, 579–584
combinators, 219
companion classes, 105
companion objects, 81, 105
and factory methods, 422
and implicit conversion, 478
visibility, 290
compare method
of trait Ordered, 265, 439
compatibility, Scala with Java, 45,
55–56
compiler errors
’;’ expected but ’,’ found, 213
. . . is already defined as . . . , 320
. . . is not a legal prefix for a
constructor, 460
overloaded method value
. . . with alternatives
. . . cannot be applied to
. . . , 152
class . . . is abstract; cannot be
instantiated, 220
class . . . needs to be abstract,
since method . . . in class
. . . of type . . . is not
defined, 455
class type required, 465
constructor . . . cannot be
accessed in . . . , 422
829
could not find implicit value for
parameter
asTraversable, 351
covariant type . . . occurs in
contravariant position in
type . . . of parameter of
setter . . . , 439
covariant type . . . occurs in
contravariant position in
type . . . of value . . . , 427,
430
error overriding method . . . in
class . . . of type . . . method
. . . cannot override final
member, 233
error overriding method . . . in
class . . . of type . . . method
. . . needs ’override’
modifier, 230
illegal inheritance from final
class, 234
inferred type arguments . . . do
not conform to method
. . . type parameter bounds
. . . , 441
method . . . overrides nothing,
229, 455
missing arguments for method
. . . follow this method with
’_’ if you want to treat it as
a partially applied
function, 191
no implicit argument matching
parameter type . . . was
found, 389, 487
not found: value . . . , 118, 191
overloaded method value
. . . with alternatives
. . . cannot be applied to
. . . , 483
pattern is unchecked since it is
eliminated by erasure, 690
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Index
reassignment to val, 67, 386
trait . . . takes type parameters,
425
type mismatch, 196, 389, 429,
457, 458, 471, 480, 496,
671, 672
unreachable code, 322
value . . . is not a member of . . . ,
140, 451
compiler warnings
match is not exhaustive, 329,
409
non variable type-argument
. . . in type pattern is
unchecked since it is
eliminated by erasure, 318,
690
Compilers: Principles, Techniques,
and Tools (Aho, et al.),
764n
compiling, 109
Scala and Java together, 709
complete function, 330
complete method
on trait Promise, 722
completeWith method
on trait Promise, 722
complexity, managing, 57
Component class, 767
composition, 225, 235
mixin, see mixin composition
concat method
on object List, 367
on Seq objects, 594
conciseness
Scala versus Java, 56–57
concurrency
actors model, 49
and testing, 715
futures, 714–735
shared data and locks, 716
concurrentmap
830
members, 569
ConcurrentMap trait
methods
remove, 569
replace, 569
ConcurrentMap trait
methods
putIfAbsent, 569
configuring
to wire modules together, 660
with Scala code, 671
cons (::) method
on class List, 82, 341, 420, 501
right associativity of, 83
short for construct, 501
Console object, 95
constant patterns, 309, 312
constants, 148
constructor patterns, 310, 315
constructors, 77
auxiliary, 142–143
in Java versus Scala, 137
invoking superclass, 228
primary, 136–138
private, 422
Container trait, 767
contains method
on Map traits, 382, 553
on Seq traits, 545
on Set traits, 380, 548
containsSlice method
on Seq traits, 545
contents property
on trait Container, 767
context bounds, 442, 493
context-free grammars, 737
continue keyword (Java)
living without, 171
contravariance, 426, 434–437
control abstraction, 53, 203–217
conventions, identifier capitalization,
147
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Index
conversion
between Java and Scala
collections, 594–596
between mutable and
immutable collections, 391
implicit, 55, 474
to specific collection types, 390,
533, 535, 588
copyToArray method
on class List, 354
on trait Iterator, 587
on Traversable traits, 536
copyToBuffer method
on trait Iterator, 587
on Traversable traits, 536
corresponds method
on Seq traits, 545
count method
on class List, 84
on trait Iterator, 589
on Traversable traits, 537
covariance, 426
of arrays in Java, 428
of function result type, 435
of lists, 341, 504
creating and initializing
arrays, 81
lists, 82
maps, 91
objects with factories, 81, 238
objects with new, 99
sets, 87
singleton objects, 107
tuples, 86
curly braces
and variable scope, 174
in class definitions, 136
in imports, 283
in XML literals, 648–649
in XML patterns, 655
instead of parentheses in
method invocations, 213
831
instead of parentheses in for
expressions, 163
currying, 209–211, 356
D
data structures
fully persistent, 419
semi-structured, 645
deadlock, 49, 715
deafTo method
on Reactor, 770
declarations, versus definitions, 806
decrementing
with -= 1, not --, see
incrementing
def keyword, 68, 99
annotating, 639
default methods
in Java 8, xxxiii, 713
default values
of fields, 400
defensive copies, 137
definitions, versus declarations, 806
dependency injection, 660
deprecated annotation, 638, 701
dequeue method
on class Queue, 560
DeRemer, Frank, 659n
derived classes, see subclasses
deserialization
XML, 653
Design Patterns (Gamma, et al.),
310n
diff method
on Seq traits, 546
on Set traits, 549
Dijkstra, Edsger, 60
distinct method
on Seq traits, 546
divide and conquer principle, 346
do-while loops, 157
domain objects, 661
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Index
Domain-Driven Design (Evans),
661n
domain-specific languages, 801
a little language for digital
circuits, 401
and implicit conversions, 482
defining an external DSL with
parser combinators,
736–746
in Smalltalk and Lisp, 62
parser combinators as an
internal DSL, 736
rich wrappers and internal
versus external DSLs, 484
ScalaTest’s matchers DSL, 300
Double class, 112
downloading
Scala, 64
source code for book examples,
xliii
drop method
on class List, 84, 350
on trait Iterator, 589
on Traversable traits, 536
dropRight method
on class List, 84
on Iterable traits, 540
dropWhile method
on class List, 360
on trait Iterator, 589
on Traversable traits, 536
DSLs, see domain-specific languages
duplicate method
on trait Iterator, 587
dynamic
binding, 231, 260
typing, 59, 61, 118
E
Eckel, Bruce, 235n
Eclipse IDE, 64, 765
EditDone class, 775
Effective C++ (Meyers), 235n
832
Effective Java, Second Edition
(Bloch), 675n, 688n, 695n
efficiency
and hash consing, 252
of abstract classes versus traits,
307n
of arrays, 81, 374
of case classes versus
extractors, 631
of class ListBuffer, 375, 506
of fold left and right, 362
of functional on the outside,
imperative on the inside
approach, 509
of immutable objects, 137
of lists, 373
of LL(1) versus backtracking
parsers, 763
of looping versus recursion, 198
of maps, 383
of merge versus insertion sort,
355
of sets, 383
of tail versus non-tail recursion,
507
of value types, 65, 81, 247, 699
Eiffel language, 62
Elem class, 647, 652
Elem type
in trait Parsers, 751
elements, XML, 646–647
else clauses, 156
embedded DSLs, see
domain-specific languages
empty method
on Map objects, 381, 383
on Seq objects, 594
on Set objects, 379, 380
on Set traits, 549
empty-paren methods, 221
enclosing instances, see inner classes
endsWith method
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Index
Prepared for Library Genesis
on Seq traits, 545
enqueue method
on class Queue, 560
ensuring method
on object Predef, 294
entities, 662
enum keyword (Java), see
Enumeration class
Enumeration class, 461
eq method
on class AnyRef, 130, 252, 674
equality, see also equals method
!= (not equals) method
on class Any, 128, 246
== (equals) method
on class Any, 128, 246, 674
eq method
on class AnyRef, 130, 252,
674
in Scala versus Java, 130
in-depth treatment, 674–697
ne method
on class AnyRef, 130, 252
quick introduction, 128–130
value versus reference, 130
equals method, see also equality
generated for case classes, 308
on class Any, 129, 674–697
contract of, 681
recipe for writing, 693–695
equals method
on class Any, 247
erasure, 319
and pattern matching, 690
Erlang language, 49–50, 53, 92
errata, viewing and reporting, xliii
errors, see also compiler errors
handling with exceptions,
165–169
in parsing, 759–761
preventing, 93
833
escape sequences, for character
literals, 117
Evans, Eric, 661n
event handling, 770–773, 796
event listeners, 770
event sources, 770
eventually method
on trait Eventually, 53
example code, for book
downloading, xliii
license of, xliii
exception handling, 165–169
and checked exceptions,
702–703
ExecutionContext, 716
exists method
on class List, 84, 208, 361
on class StringOps, 377
on trait Iterator, 589
on Traversable traits, 537
extends keyword, 223
external DSLs, see domain-specific
languages
extractors, 621–625
and injections, 625
extractor pattern, 344, 625
F
f string interpolator, 120
F# language, 53, 63
factory methods, 81, 466
factory objects, 238
collect method
on trait Future, 724
failed method
on object Future, 721
Failure class, 752
failure method
on trait Promise, 722
fallbackTo method
on trait Future, 724
false value, 119
fields, 99–101
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Index
abstract, 449
defining, 145
initializing to default values
with an underscore, 400
overriding, 225–226
parametric, 140, 226
pre-initialized, 449
private, 101, 144
FileReader class, 742
files
ensuring closed with finally,
168
opening, 95
reading lines from, 95
fill method
on object List, 366
on Seq objects, 594
filter method
on trait Future, 722
filter method
on class List, 84, 359
on trait Iterator, 589
on Traversable traits, 537
filterKeys method
on Map traits, 554
filterNot method
on trait Iterator, 589
on Traversable traits, 537
filters
in for expressions, 162, 515
final modifier, 233
on variables (Java), see vals
finally clauses, 167
find method
on class List, 360
on trait Iterator, 588
on Traversable traits, 536
findAllIn method
on class Regex, 634
findFirstIn method
on class Regex, 634
findPrefixOf method
834
on class Regex, 634
first-order methods
on class List, 345–357
flatMap method
on class List, 358
on trait Iterator, 587
on Traversable traits, 535
translating for expressions to
invocations of, 513
flatMap method
on trait Future, 719
flatten method
on trait Future, 732
flatten method
on class List, 351, 362
Flex, 736
Float class, 112
floating-point literals, 115
floatingPointNumber method
in trait JavaTokenParsers,
738, 744
fold
left, 361
right, 362
fold method
on object Future, 728
foldLeft method
on class List, 363
on trait Iterator, 590
on Traversable traits, 537
foldRight method
on class List, 363
on trait Iterator, 590
on Traversable traits, 537
for expressions, 75, 160, 513
and extractors, 636
and Futures, 719
and XML patterns, 656–658
notation, 518
translated before type checking,
526
types valid after ->, 525
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Index
using patterns in, 330, 514
for loops, see also for expressions,
523
forall method
on class List, 84, 361
on trait Iterator, 589
on Traversable traits, 537
force method
on views, 580
Prepared for Library Genesis
foreach method
on trait Future, 730
foreach method
on class Array, 74
on class List, 84, 359
on Traversable traits, 535
overriding, 614
translating for expressions to
invocations of, 513
formatted method
on Any (via StringAdd), 468
formatting strings, 468
Fortran language, 48
Fowler, Martin, 660n
free variables, 191
fromTry method
on object Future, 721
fsc (fast Scala compiler), 109
fully qualified names, 89
function literals
and closures, 193
and single abstract method
types, 710
for partial functions, 327
placeholder syntax, 187, 206
syntax of, 75
underscores in, 187, 206
versus function values, 184
function values, 184, 205
and closures, 193
functional programming, 45, 52–54,
135
lazy initialization, 454
835
main ideas of, 53
functional style, 87
combining with imperative
style, 509
in parsing, 751
methods without side effects, 81
recognizing, 92
refactoring functional style to,
177
functions, see also methods
basic form of, 69
first-class, 53, 184
higher-order, 203
local, 182
partial, 327–330
partially applied, 188, 356
predicates, 58
procedures, 103
pure, 222
recursive, 198
tail recursive, 199
future method
on trait Promise, 721
Future object
methods
apply, 716
failed, 721
fold, 728
fromTry, 721
reduce, 729
sequence, 729
successful, 721
traverse, 730
Future trait
methods
andThen, 731
collect, 723
failed, 724
fallbackTo, 724
filter, 722
flatMap, 719
flatten, 732
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Index
foreach, 730
isCompleted, 717
map, 719
onComplete, 731
recoverWith, 726
recover, 725
transformWith, 733
transform, 726
value, 717
withFilter, 723
zipWith, 732
zip, 728
Prepared for Library Genesis
futures, 714–735
G
Gamma, Erich, 310n
generator, in for expressions, 514
generic arrays, 572
generic types, 418, 425
get method
on Map traits, 553
getLines method
on object Source, 95
getOrElse method
on Map traits, 553
getOrElseUpdate method
on trait mutable.Map, 554
getter methods, 398, 431, 446, 643,
767
Gosling, James, 428, 518
graphical user interfaces, see user
interfaces
groupBy method
on Traversable traits, 537
grouped method
on Iterable traits, 540
on trait Iterator, 587
“Growing a language” (Steele), 47n
GUI programming, see user
interfaces
Guice, 660
836
H
handlers, exception, see catch
clauses
hasDefiniteSize method
on trait Iterator, 588
on Traversable traits, 536
hash consing, 252
hashCode method
caching, 697
generated for case classes, 308
if overriding equals, 678–679
on class Any, 246
contract of, 679
recipe for writing, 695–697
HashMap class
in class hierarchy diagram, 90
HashSet class, 87
creating and initializing
immutable, 89
in class hierarchy diagram, 88
Haskell language, 48, 53, 92
as a lazy functional language,
454
hasNext method
on trait Iterator, 587
head method
on class List, 85, 342, 500
on Traversable traits, 536
headOption method
on Traversable traits, 536
Hejlsberg, Anders, 441
Hibernate, 662
hiding
names in import clauses, 284
names in nested packages, 281
variables in nested blocks, 176
higher-kinded types, see http://
booksites.artima.com/
programming_in_scala_3ed
higher-order functions, 203
higher-order methods
on class List, 357–365
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Prepared for Library Genesis
Index
I
identifiers, 147–149
alphanumeric, 147
capitalization conventions for,
147
literal (with back ticks), 149
mixed, 149
operator, 148
identity
of objects, testing with eq
method, 674
idioms, see patterns and idioms
if expressions, 69, 73, 156–157
immutability, 54
as functional style, 81
selecting mutable versus
immutable collections, 385
trade-offs, 137
imperative style, 87
combining with functional
style, 509
recognizing, 92
refactoring to functional style,
177
implementation traits, 599
implements keyword (Java), see
mixin composition
implicit keyword, 152
implicit classes, 484
implicit conversion, 55, 295
a word of caution, 153
implicit classes, 484
in-depth treatment, 474–484
quick introduction, 152
to rich wrappers, 133
implicit conversions
when multiple apply, 494
implicit parameters, 485–491
implicit resolution, 603
imports, 89, 281–286
hiding clauses, 284
implicit, 108, 285–286
837
on-demand (i.e., wildcard), 282
renaming clauses, 283
selector clauses, 283
incrementing
with += 1, not ++, 72
IndexedSeqLike implementation
trait, 599
indexOf method
on Seq traits, 544
on trait Iterator, 588
indexOfSlice method
on Seq traits, 544
indexWhere method
on Seq traits, 544
on trait Iterator, 589
indices method
on class List, 351
on Seq traits, 544
inference
semicolon, 104–105
turning off, 748
type, 61, 91n
Hindley-Milner style, 371
the algorithm, 368
infix operator notation, 121
information hiding, 65, 422
with factory methods, 238
inheritance, 270
and self types, 790
extending classes, 223
mixing in traits, 258
versus composition, 235
inheritance hierarchy diagram, see
class hierarchy diagram
init method
on class List, 85, 348
on Traversable traits, 536
initialization
lazy, 451
of abstract vals, 447
of arrays, 81
of classes, see constructors
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Prepared for Library Genesis
Index
of fields to default values, 400
of instance variables with
constructor parameters, see
class parameters
of lists, 82
of maps, 91
of sets, 87, 89
of traits, 447
initializers, 145
injections, 625
inner classes
accessing outer class members
with a this alias, 753
and path-dependent types, 459
and private members, 286
example of, 782
granting access to private
members of inner classes,
289
private, to hide implementation,
425
insert method
on trait Buffer, 547
insertAll method
on trait Buffer, 547
insertion sort, 342, 344
instance variables, 101
instanceOf (Java), see type tests
Int class, 112
integer literals, 114
IntelliJ IDE, 64
interfaces (Java), see traits
internal DSLs, see domain-specific
languages
interoperability, Scala with Java, 45
interpolated string literals, 119
interpreter, 64–66
intersect method
on Seq traits, 546
on Set traits, 549
invoking a superclass constructor,
228
838
is-a relationships, 235
isCompleted method
on trait Future, 717
isDefinedAt method
on Map traits, 553
on Seq traits, 544
on trait PartialFunction, 329
isEmpty method
on class List, 85, 342, 500
on Map traits, 383
on trait Iterator, 588
on Traversable traits, 536
isInstanceOf method
on class Any, 317
iterable
members, 540
Iterable traits
methods
dropRight, 540
grouped, 540
iterator, 540
sameElements, 540
sliding, 540
takeRight, 540
zip, 540
zipAll, 540
zipWithIndex, 540
iterate method
on Seq objects, 594
iterator
members, 587
iterator method
on class List, 354
on Iterable traits, 540
on mutable map, 618
Iterator trait
methods
/: (fold left), 590
:\ (fold right), 590
++ (add elements), 587
addString, 591
buffered, 587
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Prepared for Library Genesis
Index
collect, 588
copyToArray, 587
copyToBuffer, 587
count, 589
drop, 589
dropWhile, 589
duplicate, 587
exists, 589
filter, 589
filterNot, 589
find, 588
flatMap, 587
foldLeft, 590
foldRight, 590
forall, 589
grouped, 587
hasDefiniteSize, 588
hasNext, 587
indexOf, 588
indexWhere, 589
isEmpty, 588
length, 588
map, 587
max, 590
min, 590
mkString, 591
next, 587
nonEmpty, 588
padTo, 587
partition, 589
patch, 590
product, 590
reduceLeft, 590
reduceRight, 590
sameElements, 591
size, 588
slice, 589
sliding, 587
sum, 590
take, 589
takeWhile, 589
toArray, 588
839
toIndexedSeq, 588
toIterable, 588
toList, 588
toMap, 588
toSeq, 588
toSet, 588
toStream, 588
withFilter, 589
zip, 590
zipAll, 590
zipWithIndex, 590
J
Java 8, xxxiii, 709
default methods, xxxiii, 713
lambda expressions, xxxiii, 710
Stream interface, 712
Java language, 54, 92
javac, 704
ANTLR, 736
bytecodes, 698
collections, 594–596
compiling with Scala, 709
concurrency utilities, 715
covariance of arrays, 428
dependency injection in, 660
enumerations, 461
exception handling, 702
inner classes, 459
interfaces and Scala traits, 701
interoperability with Scala, 45,
55–56, 698–713
raw types, 418, 706
static methods, 699
Swing framework, 765–776
threading model, 715
throws keyword, 702
value types, 699
wildcard types, 706
wildcards, 434
Java Native Interface, 644
Java Persistence Architecture, 662
JavaBeans, 86
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Index
JavaConversions object, 594
JavaScript Object Notation, see
JSON
JavaTokenParsers trait, 738
JFlex, 736
JNI, 644
Joy, Bill, 518
JSON, 741–746
JUnit, 703–704
K
Kay, Alan, 51, 441
keys method
on Map traits, 382, 553
keySet method
on Map traits, 383, 553
keysIterator method
on Map traits, 553
keywords, using as identifiers, 149
L
Prepared for Library Genesis
Label class, 782
LALR(1) parsers, 764
lambda calculus, 53
lambda expressions
in Java 8, xxxiii, 710
Landin, Peter, 62
last method
on class List, 85, 348
on Traversable traits, 536
lastIndexOf method
on Seq traits, 544
lastIndexOfSlice method
on Seq traits, 544
lastOption method
on Traversable traits, 536
laws, 219
layouts, in GUIs, 769
lazy modifier, 452
lazy functional languages, 454
length method
on class List, 85, 347
on trait Iterator, 588
840
lengthCompare method
on Seq traits, 544
Lex, 736
lexical analysis, 751, 759
linearization, of traits, 270–274
linked lists, 340
linking
of modules at runtime, 670
Liskov Substitution Principle, 434
and equals method, 688
Lisp language, 48, 53
List class, see also lists, 82, 373
first-order methods on, 345–357
higher-order methods on,
357–365
immutability, 82
implementation of, 499–510
methods
/: (fold left), 361
::: (concatenation), 82, 345,
501
:\ (fold right), 362
:: (cons), 82, 342
addString, 353
apply, 351
copyToArray, 354
count, 84, 347, 350
drop, 84, 350
dropRight, 84
dropWhile, 360
exists, 84, 361
filter, 84, 359
find, 360
flatMap, 358
flatten, 351, 362
foldLeft, 363
foldRight, 363
forall, 84, 361
foreach, 84, 359
head, 85, 342
indices, 351
init, 85, 348
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Index
isEmpty, 85, 342
iterator, 354
last, 85, 348
length, 85
map, 85, 357
mkString, 85, 353
partition, 359
reduceLeft, 97
remove, 85
reverse, 85, 349
sort, 85
sortWith, 365
span, 360
splitAt, 350
tail, 85, 342
take, 350
takeWhile, 360
toArray, 354
toString, 353
unzip, 352
zip, 352
zipWithIndex, 352
List object
methods
Prepared for Library Genesis
apply, 82, 365, 630
concat, 367
fill, 366
range, 358, 366
tabulate, 367
ListBuffer class, 83, 375–376, 506
listenTo method
on Reactor, 770, 798
lists, see also List class, 81–83, 340
appending to, 83
class hierarchy diagram, 500
concatenation (:::), 345
converting to arrays, 390
converting to strings, 353
creating and initializing, 82
diagram showing linked-list
structure, 504
in-depth treatment, 340–372
841
passing elements as arguments
with _*, 196
table of methods, 84
ListView class, 779
literal method
on trait RegexParsers, 758
literals, 113–119
array, 81
boolean, 119
character, 116
class, see classOf method
floating point, 115
identifier, 149
integer, 114
interpolated string, 119
list, 82
string, 117, 119
symbol, 118
XML, 647–649
little languages, see domain-specific
languages
LL(1) grammars, 762
loadFile method
on XML, 654
loan pattern, 168, 212
local functions, 182
local variables, 174
locks, and shared data, 716
logical operations (&&, ||, !)
on class Boolean, 126
Long class, 112
lower bounds, 432
LowPriorityImplicits class, 496
LSP, see Liskov Substitution
Principle
M
magic numbers, 148
main method
of GUI applications, 766
of Scala applications, 108
MainFrame class, 767
attributes
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Index
contents, 767
title, 767
Prepared for Library Genesis
map
members, 553
map method
on class List, 85, 357
on trait Iterator, 587
on trait Future, 719
on Traversable traits, 535
translating for expressions to
invocations of, 513
Map objects, 91, 381
Map traits, see maps
methods
++ (add elements), 553
- (remove element), 553
-- (remove elements), 553
++= (add elements to), 554
+= (add element to), 554
--= (remove elements from),
554
-= (remove element from),
554
apply, 553
clear, 555
clone, 555
contains, 553
filterKeys, 554
get, 553
getOrElse, 553
getOrElseUpdate, 554
isDefinedAt, 553
keys, 553
keySet, 553
keysIterator, 553
mapValues, 554
put, 554
remove, 554
retain, 555
update, 554
updated, 553
values, 553
842
valuesIterator, 553
Map traits
methods
+ (add element), 553
maps, 89–91, 377–381
class hierarchy diagram, 90
creating and initializing
immutable, 91
creating and initializing
mutable, 90
in library, not language, 46
mutability modeled in
hierarchy, 87
table of methods, 382
mapValues method
on Map traits, 554
match expressions
in-depth coverage, 308–325
quick introduction, 169–170
MatchError class, 310
and “not exhaustive” warning,
324
and extractors, 624
not thrown in for expressions,
514, 522
Matsumoto, Yukihiro, 441
max method
on class Int (via RichInt), 97,
134, 249
on trait Iterator, 590
on Traversable traits, 538
member functions, see methods
member variables, see fields
members
abstract, 443
field, 99
initialization order of, 449
method, 99
no static, 105
type, 444
merge sort, 355
message passing, 49
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Prepared for Library Genesis
Index
meta-programming, 638
methods, see also functions, 99, 180
abstract, 220
as operators, 121
as procedures, 103
empty-paren, 221
first-order, 345
native, 644
overloading, 150
overriding, 225–226
parameterless, 220–223
overriding with vals, 445,
502
parameters of, 102
private, 144
return from, 102
when to invoke with empty
parentheses, 123, 221
Meyer, Bertrand, 221n
Meyers, Scott, 235n
min method
on class Int (via RichInt),
134, 249
on trait Iterator, 590
on Traversable traits, 538
mixed identifiers, 149
mixin composition, see also traits
and self types, 669, 800
example of, 793
syntax for, 258
mkString method
on class List, 85, 353
on trait Iterator, 591
on Traversable traits, 93, 538
mock objects, 662
modules, 659–673
runtime linking, 670
monads, 527
monitors, 714
Multi-Java language, 63
multi-threaded programming, see
concurrency
843
multicore, 49
multiple return values, 86, 392
multiple variable definitions, 393
multiple-line statements, 104
mutability
and equals, 680–681
of collections, 386
of object state, 395
N
named arguments, 196
namespaces
in C#, 277
Scala’s two instead of Java’s
four, 226
naming conventions, 147
NaN (not a number), 791, 797
native annotation, 644
ne method
on class AnyRef, 130, 252
nested annotations, 640
nesting
of classes and objects, 240
of functions, 182
of packages, 277
.NET, 55, 250, 285
NetBeans IDE, 64
new keyword, 99
not needed when calling factory
methods, 81
not needed with case classes,
307
next method
on trait Iterator, 587
Nice language, 63
Nil object, 83, 341, 499–501
Nilsson, Rickard, 302
Node class, 647
NodeSeq class, 648
nominal subtyping, 460
None object, 325
in extractors, 624
nonEmpty method
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Index
on trait Iterator, 588
on Traversable traits, 536
nonvariance, 426
Nothing type, 252
as result type of throw
expressions, 166
as type parameter of List, 341
in Scala class hierarchy
diagram, 248
Null class, 252
in Scala class hierarchy
diagram, 248
null value, 252
in equals methods, 681
using Option instead for
optional values, 326
NullPointerException,
preventing, 326
O
Prepared for Library Genesis
Object class, see also AnyRef class
in Scala class hierarchy
diagram, 248
object keyword, 105–107
object-oriented programming, 45,
51–52, 135
Object-Oriented Software
Construction (Meyer),
221n
object-private access with
private[this], 289
objects
annotating, 639
companion, see companion
objects
domain, 661
entity, 662
equality of, see equality
functional (immutable), 135
immutable, 81
mock, in testing, 662
mutable, 395
serializing, 642
844
singleton, see singleton objects
standalone, 107
OCaml language, 53, 63, 92
on-demand import, 282
onComplete method
on trait Future, 731
operator
associativity, 132–133
example: class Rational with
operator methods, 151
identifiers, 148
when to use, 750
notation, 79–80, 121–123, 147
infix, 121
postfix, 123
prefix, 122
“overloading”, 79
precedence, 130–132, 147
table of, 131
opt method
on trait Parsers, 757
optimization
of tail recursion, 201
turning off with
notailcalls, 201
of value types, 250
Option class, 325–326
Ordered trait, 264–266, 385, 439
outer classes, see inner classes
overloading
constructors, 142–143
methods, 150
“operator”, 79
override modifier, 138, 229
on parametric fields, 227
overriding
equals method, 677
defs with vals, 445, 502
methods and fields, 225–226
P
package objects, 291
package clauses, 277
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Prepared for Library Genesis
Index
package-private access (Java), 289
packages, 276–281
as modules, 659
hiding, 281
implicitly imported, 285–286
unnamed package, 276
packaging, 277
padTo method
on Seq traits, 544
on trait Iterator, 587
pair, name for Tuple2, 237
Panel class, 769
parallel collections, 46, 529
parallel programming, 714–735
parameterization
with types, 77
with values, 77
parameterized types, 77
and object equality, 688–692
parameterless methods, 220–223
overriding with vals, 445, 502
parameters
by-name, 215, 407
by-value, 756
class, see class parameters
default values, 197
implicit, 442, 479, 485–491
named, 196
repeated, 81, 195–196
vararg matching, 628
type, 77
versus arguments, 810
parametric fields, 140, 227
parentheses
using curly braces instead of,
213
when to use on method calls,
123, 221
Parser class, 738
parser combinators, 736–764,
785–788, 801
parser generators, 736
845
ParseResult class, 751
parsers, 736–764
Parsers trait, 750, 757
partial functions, 327–330
PartialFunction trait, 329, 771
partially applied functions, 188, 356
partition method
on class List, 359
on trait Iterator, 589
on Traversable traits, 537
passing arguments
to superclass a constructor, 228
patch method
on Seq traits, 545
on trait Iterator, 590
path-dependent types, 457–460
and inner classes, 459
Patricia tries, 614
pattern guards, 321
pattern matching
and extractors, 621
constant patterns, 309, 312
constructor patterns, 310, 315
examples of, 408, 790
in event handling, 771
in exception handling, 166
in for expressions, 330, 514
in lists, 343, 501
in variable definitions, 327
in equals method, 678, 682
in-depth coverage, 308–331
on XML, 655–658
quick introduction, 169–170
sequence patterns, 315
tuple patterns, 316
typed patterns, 317
variable patterns, 309, 313
variable-binding patterns, 320
wildcard patterns, 310, 311
patterns and idioms
conditional initialization, 156
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Prepared for Library Genesis
Index
divide and conquer principle,
346
ensuring a resource is closed,
168
functional on the outside,
imperative on the inside,
509
loan pattern, 168, 212
reading lines, characters, etc.,
with do-while, 158
rich wrappers pattern, 133–134,
484
using Option not null to
indicate optional values,
326
performance, see also efficiency
and multi-core processors, 49
of collection types, 575–579
of Scala compared to Java, 55
Perl language, 46, 632
Pizza language, 63
placeholder syntax
in wildcards, 706
in function literals, 187, 206
PLT-Scheme language, 63
pointers, see references
polymorphism, 231
and type inference, 370
pop method
on class Stack, 559
postconditions, see ensuring
method
postfix operator notation, 123
pre-initialized fields, 449
precedence, operator, 130–132, 147
table of, 131
preconditions, 139
Predef object
methods
???, 347
assert, 94, 294
classOf, 703
846
ensuring, 294
println, 108
predicates, 58
prefix operator notation, 122
prefixLength method
on Seq traits, 544
primary constructors, 136–138
private, 422
primitive types, 81
private modifier
and inner classes, 286
augmented with qualifiers, i.e.,
private[meeting], 288
granting access to private
members of inner classes,
289
in modules, 663
object-private access with
private[this], 289
on constructors, 422
on fields, 101, 144
on methods, 144
on parametric fields, 227
procedures, 103
processed string literals, 119
product method
on trait Iterator, 590
on Traversable traits, 538
programming in the large, 659
programming in the small, 659
programming style
functional, 87
imperative, 87
modular, 659–673
Promise trait, 721
methods
completeWith, 722
complete, 722
failure, 722
future, 721
success, 722
pronunciation of Scala, 45
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Index
properties, 149, 398–401, 767
verifiable with static typing, 59
protected modifier, 287
on parametric fields, 227
public access, 101
Publisher trait, 796
push method
on class Stack, 559
put method
on trait mutable.Map, 554
putIfAbsent method
on trait ConcurrentMap, 569
Python language, 46, 48
Q
Queue class, 560
R
Prepared for Library Genesis
r method
on class String (via
StringOps), 634
range method
on object List, 358, 366
on Seq objects, 594
ranges, of basic types, 113
raw string interpolator, 120
raw strings, 117
in regular expressions, 633
raw types (Java), 418, 706
Raymond, Eric, 47
reactions property
on Reactor, 771, 789
Reactor trait, 770
Reader class, 751
reading
from standard input with
do-while, 158
lines from a file, 95
receive method
on trait Actor, 49
recommended style, see style
recommendations
recover method
847
on trait Future, 725
recoverWith method
on trait Future, 726
recursion, see recursion
in combinator parsers, 756
tail, 198–202
optimization, 201
using ListBuffer to prevent
stack overflow, 375
reduce method
on object Future, 729
reduceLeft method
on class List, 97
on trait Iterator, 590
on Traversable traits, 538
reduceRight method
on trait Iterator, 590
on Traversable traits, 538
refactoring
and static typing, 60
and vals, 157
imperative to functional style,
177
reducing code duplication with
closures, 206
reference
classes, 250
definition of, 812
equality, 130, 251, 674
referential transparency, 54
refinement types, 460
reflection, 704
Regex class, 633
regex method
on trait RegexParsers, 758
RegexParsers trait, 741, 758, 785
regular expressions, 632
as parsers, 740, 785–788
relational operations (>, <, >=, <=)
on numeric types, 125
on trait Ordered, 264–266
remove method
Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
Prepared for Library Genesis
Index
on class List, 85
on trait ConcurrentMap, 569
on trait mutable.Map, 554
on trait mutable.Set, 550
on trait Buffer, 547
rendererComponent method
on class Table, 780
rep method
on trait Parsers, 757
repeated parameters, 81, 195–196
vararg matching, 628
replace method
on trait ConcurrentMap, 569
representation independence, 631
repsep method
on trait Parsers, 757
require method
on object Predef, 139
resources, ensuring release of, 168,
212
result type, 68
covariance of, 435
retain method
on trait mutable.Map, 555
on trait mutable.Set, 550
return keyword, 102
return type, 68
returning multiple values, 86, 392
reverse method
on class List, 85, 349
on Seq traits, 545
reverseIterator method
on Seq traits, 545
reverseMap method
on Seq traits, 545
rich wrappers pattern, 133–134, 484
RichInt class, see Int class
rowHeaderView property
on class ScrollPane, 779
Ruby language, 46, 54, 474
Rumbaugh, James, 272
running a Scala program, see scala
848
runtime type, definition of, 813
S
s string interpolator, 119
SAM types, see single abstract
method types
sameElements method
on Iterable traits, 540
on trait Iterator, 591
save method
on XML, 654
scala (Scala launcher)
to launch applications, 110
to launch scripts, 71
to launch the interpreter, 65
Scala language
2.12 release, xxxii, xxxvii, 476,
709–713, 727–733
class hierarchy diagram, 248
compatibility with Java, 45,
55–56, 698–713
compiling, 109
with Java, 709
conciseness versus Java, 56–57
downloading, 64
IDEs, 64
influences on, 61–63
interpreter shell, 64–66
naming of source files, 109
pronunciation of “Scala”, 45
scalability of, 51–54, 661, 800
scripts, see scripts, Scala
specification, xxxviii
Scala.js, 55, 716
scalability
of configuration, 671
of Scala language, 51–54, 661,
800
of teams, 660
scalac (Scala compiler), 109
ScalaCheck, 296, 302–304
Scaladoc, 637
ScalaTest, 53, 296–304
Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
Prepared for Library Genesis
Index
scanner generators, 736
Scheme language, 53
scope
of implicit conversions, 477
of variables, 173–177
scripts, Scala, 70–71, 109
running on Unix, 802
running on Windows, 802
ScrollPane class, 777, 779
sealed classes, 323–325
sealed modifier, 323, 631
segmentLength method
on Seq traits, 544
selectors
in match expressions, 309
selectors, in imports, 283
self references, 141–142
self types, 669, 790, 794
semi-structured data, 645
semicolon inference, 104
turning off, 748
seq
factory, 594
members, 544
Seq objects
methods
apply, 594
concat, 594
empty, 594
fill, 594
iterate, 594
range, 594
tabulate, 594
Seq traits, 236
methods
+: (prepend), 544
:+ (append), 544
apply, 544
contains, 545
containsSlice, 545
corresponds, 545
diff, 546
849
distinct, 546
endsWith, 545
indexOf, 544
indexOfSlice, 544
indexWhere, 544
indices, 544
intersect, 546
isDefinedAt, 544
lastIndexOf, 544
lastIndexOfSlice, 544
lengthCompare, 544
padTo, 544
patch, 545
prefixLength, 544
reverse, 545
reverseIterator, 545
reverseMap, 545
segmentLength, 544
sortBy, 545
sorted, 545
sortWith, 545
startsWith, 545
union, 546
update, 545
updated, 545
sequence method
on object Future, 729
sequence patterns, 315
serializable annotation, 642, 702
serialization
binary, 642
to and from XML, 649–651
SerialVersionUID annotation, 642
set
members, 548
Set objects, 87, 379
Set traits, see sets
methods
++= (add elements to), 549
++ (add elements), 548
+= (add element to), 549
Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
Index
Prepared for Library Genesis
--= (remove elements from),
550
-- (remove elements), 549
-= (remove element from),
549
- (remove element), 549
&~ (diff), 549
& (intersect), 549
| (union), 549
+ (add element), 548
add, 549
clear, 550
clone, 550
contains, 548
diff, 549
empty, 549
intersect, 549
remove, 550
retain, 550
subsetOf, 548
union, 549
update, 550
sets, 87–89, 377
and generic types, 418
class hierarchy diagram, 88
creating and initializing
immutable, 87
creating and initializing
mutable, 89
mutability modeled in
hierarchy, 87
table of methods, 380
setter methods, 398, 446, 643, 767
shadowing variables, 176
shared data and locks, 716
shell, Scala interpreter, 64–66
shift operations (<<, >>, >>>)
on integral types, 128
short circuiting, of logical
operations, 126
Short class, 112
side effects, 54, 81, 93
850
minimizing, 94
SimpleSwingApplication class,
766, 779
Simula language, 51
single abstract method types, 710
singleton objects, 105–107
as modules, 659
as seen from Java, 700–701
in testing, 662
initialization semantics, 107
nesting of, 240
shown with rounded corners in
hierarchy diagrams, 500
singleton types, 564n, 673
size method
on Set traits, 380
on trait Iterator, 588
on Traversable traits, 536
slice method
on trait Iterator, 589
on Traversable traits, 536
sliding method
on Iterable traits, 540
on trait Iterator, 587
Smalltalk language, 51, 474
SML language, 53
snoc, 364
Some class, 325
in extractors, 624
sort
insertion, 342, 344
merge, 355
sort method
on class List, 85
sortBy method
on Seq traits, 545
sorted method
on Seq traits, 545
SortedMap trait, 385
SortedSet trait, 385
sortWith method
on class List, 365
Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
Prepared for Library Genesis
Index
on Seq traits, 545
source code, downloading, see
example code
source files, naming of, 109
Source object, 95
span method
on class List, 360
on Traversable traits, 537
specifiers, access, see access
modifiers
specs2, 296, 301
split method
on class String, 379
splitAt method
on class List, 350
on Traversable traits, 537
spreadsheets, 777–801
Spring Framework, 660, 671
Stack class, 559
stack, the
and tail-recursive functions,
199, 506
and thrown exceptions, 166
and variables captured by
closures, 195
stackable modifications, 266–270
standalone objects, 107
standard error stream, 95
startsWith method
on Seq traits, 545
statements
multiple-line, 104
static members, 105
static typing, 58–61
Steele, Guy, 47, 518
Stream class
in Scala, 546, 557
Stream interface
in Java 8, 712
String class, 113
methods
r (via StringOps), 634
851
stripMargin (via
StringOps), 118
StringBuilder class, 353
stringLiteral method
in trait JavaTokenParsers,
744
stringly typed code, 255
StringOps class, see also String
class, 377, 496
stringPrefix method
on Traversable traits, 538
strings
formatting, 468
interpolated (processed), 119
iterating through the characters
of, 58
literals, 117, 119
long, 117
raw, 117, 633
treating as a sequence (via
WrappedString), 496
treating like a sequence (via
StringOps), 496
treating like a sequence (via
StringOps), 377
structural subtyping, 460
Structure and Interpretation of
Computer Programs
(Abelson, et al.), 401
style recommendations
prefer alphabetic names unless
symbolic names have
established meaning, 750
prefer classes to tuples when
data combination has
meaning, 393
prefer vals, immutable objects,
and methods without side
effects, 94
use implicit conversions with
restraint, 153
subclasses
Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
Index
and equals method, 685
defining, 223
subsetOf method
on Set traits, 548
subtyping
and type parameters, 425
nominal vs. structural, 460
versus subclassing, 814
subtyping polymorphism, 231
Success class, 751
success method
on trait Promise, 722
successful method
on object Future, 721
sum method
on trait Iterator, 590
on Traversable traits, 538
super references
special treatment in traits, 260
superclasses, 223
invoking constructor on, 228
supertype, 436
Sussman, Gerald J., 401, 518
Swing, 765–776
Swing class, 769
methods
EmptyBorder, 769
switch statement (Java), 169
symbol literals, 118
symbolic versus alphabetic names,
750
syntactical analysis, 759
Prepared for Library Genesis
T
Table class, 777, 780
TableUpdated class, 788
tabulate method
on object List, 367
on Seq objects, 594
tags, XML, 646
tail method
on class List, 85, 342, 500
on Traversable traits, 536
852
tail recursion, 198–202, 505
optimization, 200
turning off with
notailcalls, 201
verifying with @tailrec,
643
tailrec annotation, 643
take method
on class List, 350
on trait Iterator, 589
on Traversable traits, 536
takeRight method
on Iterable traits, 540
takeWhile method
on class List, 360
on trait Iterator, 589
on Traversable traits, 536
target typing, 186
template, definition of, 815
term, in function literals, 193
Test annotation
in JUnit 4, 704
test suites, 304
testing, 94, 296–305
integration, 660
unit, 177
with mock objects, 662
with singletons, 662
Text class, 647
text method
on class Node, 651
text processing in XML, 646, 651
TextField class, 781
Thinking in Java (Eckel), 235n
this, 141–142
aliasing, 753
and self types, 669
throw expressions, 166
throws annotation, 702
tiny types, 255
to method
Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
Index
on class Int (via RichInt), 79,
134, 249
toArray method
on class List, 354
on trait Iterator, 588
on Traversable traits, 535
toChar method
on class Int, 784
toIndexedSeq method
on trait Iterator, 588
on Traversable traits, 535
toInt method
on class String (via
StringOps), 55
toIterable method
on trait Iterator, 588
on Traversable traits, 535
toList method
on class Array, 354, 390
on class ListBuffer, 375, 506
on trait Iterator, 588
on Traversable traits, 535
toMap method
on trait Iterator, 588
on Traversable traits, 535
top method
on class Stack, 559
top method
on class
Prepared for Library Genesis
SimpleSwingApplication,
766
Torreborre, Eric, 301
toSeq method
on trait Iterator, 588
on Traversable traits, 535
toSet method
on trait Iterator, 588
on Traversable traits, 535
toStream method
on trait Iterator, 588
on Traversable traits, 535
toString method, 138, 246
853
generated for case classes, 308
on class Any, 246
overriding, 138
total function, see complete function
traits, see also mixin composition,
257, 667–670
and abstract parameters, 447
and Java interfaces, 701
annotating, 639
generic, 418, 425
implementation, 599
initializing, 447
linearization of, 270–274
mixing in, 87
transform method
on trait Future, 726
transformers, 579
transformWith method
on trait Future, 733
transient annotation, 643
Traversable traits
methods
/: (fold left), 537
:\ (fold right), 537
++ (add elements), 535
addString, 538
collect, 535
copyToArray, 536
copyToBuffer, 536
count, 537
drop, 536
dropWhile, 536
exists, 537
filter, 537
filterNot, 537
find, 536
flatMap, 535
foldLeft, 537
foldRight, 537
forall, 537
foreach, 535
groupBy, 537
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Index
Prepared for Library Genesis
hasDefiniteSize, 536
head, 536
headOption, 536
init, 536
isEmpty, 536
last, 536
lastOption, 536
map, 535
max, 538
min, 538
mkString, 538
nonEmpty, 536
partition, 537
product, 538
reduceLeft, 538
reduceRight, 538
size, 536
slice, 536
span, 537
splitAt, 537
stringPrefix, 538
sum, 538
tail, 536
take, 536
takeWhile, 536
toArray, 535
toIndexedSeq, 535
toIterable, 535
toList, 535
toMap, 535
toSeq, 535
toSet, 535
toStream, 535
view, 538, 580
withFilter, 537
TraversableLike implementation
trait, 599
traverse method
on object Future, 730
TreeMap class, 385
TreeSet class, 385
trimEnd method
854
on trait Buffer, 547
trimStart method
on trait Buffer, 547
true value, 119
Try class
class hierarchy diagram, 718
Try class, 716–718
try expressions, 165–169
tuple patterns, 316
tuples, 392–394
creating and using, 86
one-based element access, 87
result of -> operation, 91
when to use a class instead, 393
zipped method on, 367
type aliases, 444
type annotations, 46, 60, 66
for debugging, 371
type casts, see casting
type constructors, 425
type erasure, 319
type inference, 61, 91n
Hindley-Milner style, 371
the algorithm, 368
type keyword
in singleton types, such as
db.type, 673
in type alias definitions, 378,
444
type members, 444–445
type parameterization, see
parameterized types
type parameters, 346, 418
in parser results, 751
lower bounds, 432
upper bounds, 439
type tests
with isInstanceOf, 317
with typed patterns (preferred
style), 317
type-driven design, 433
typed patterns, 317
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Index
Prepared for Library Genesis
types
abstract, 455
bottom, 252
erasure, 690
generic, 418
ground, 816
in for expressions, 526
integral, 112
numeric, 112
path-dependent, 457–460
primitive, 81
refinement, 460
result, 68
return, 68
runtime, 816
self, 669, 790, 794
singleton, 673
static, 816
wildcard, 692, 706–709
typing
dynamic, 59, 61, 118
static, 58–61
target, 186
U
Ullman, Jeffrey, 518
unapply method
in extractors, 622
unapplySeq method
in extractors, 628
unary operations, 122, 149
unchecked annotation, 325, 409,
639, 644
Unicode, 116
Unified Modeling Language
Reference Manual, The
(Rumbaugh, et al.), 272n
uniform access principle, 221
union method
on Seq traits, 546
on Set traits, 549
Unit class, 93
unit testing, 94, 177, 296–305
855
unit value, 158
unnamed package, 276
until method
on class Int (via RichInt),
237, 249, 392
unzip method
on class List, 352
update method
on class Array, 80
on Seq traits, 545
on trait mutable.Map, 554
on trait mutable.Set, 550
updated method
on Map traits, 553
on Seq traits, 545
upper bounds, 439
user interfaces, 765–801
reactive programming, 777, 801
V
vals, see also variables
abstract, 445
annotating, 639
as fields, 99
conditional initialization of, 156
defining, 66–68
lazy, 452
preferring over vars, 92
referencing mutable objects, 78
“val” on class parameters, 227
Value class, 462
value classes, 247
value method
on trait Future, 717
value parameterization, 77
ValueChanged class, 797
values method
on class Enumeration, 462
on Map traits, 383, 553
valuesIterator method
on Map traits, 553
van Rossum, Guido, 441
vararg matching, 628
Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
Prepared for Library Genesis
Index
variable patterns, 309, 313
variable-binding patterns, 320
variable-length arguments, or
“varargs”, see repeated
parameters
variables
bound, 163, 191
captured by closures, 195
defining, 66–68
using patterns, 327, 393
fields, 99–101
final (Java), see vals
free, 191
instance, 101
local, 174
multiple definitions of, 393
reassignable, see also vars, 398
scope, 173–177
shadowing, 176
variance, 426
annotations, 425–429
compiler checking of,
429–432, 439
declaration-site, 434
use-site, 434
vars, see also variables
abstract, 446
and mutable objects, 397
annotating, 639
as fields, 99
defining, 66–68
on class parameters, 227
programming without, 92
Vector class, 607
verifiable properties, 59
view method
on Traversable traits, 538,
580
viewportView property
on class ScrollPane, 779
views, 579–584
virtual classes, 466
856
volatile annotation, 642, 701
W
Wall, Larry, 441
warnings, see compiler warnings
weak hash maps, 568
while loops, 71–73, 157–159
as imperative style, 92
wildcard patterns, 310, 311
wildcard types, 692, 706–709
wildcard types (Java), 706
Wirth, Niklaus, 518
with keyword, 259
withFilter method
on trait Future, 723
on trait Iterator, 589
on Traversable traits, 537
translating for expressions to
invocations of, 513
WrappedString class, 496
X
XML, 645–658
attributes, 647, 652
character encoding, 654
configuration files, 660, 671
deserialization, 653
elements, 646–647
literals, 647–649
pattern matching, 655–658
serialization, 649–651
tags, 646
text, 646
using an at sign (@) to extract
attributes , 652
using curly braces ({}) in
pattern matching, 655
XPath, 651
XML object
methods
loadFile, 654
save, 654
XPath, 651
Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
Index
Y
Yacc, 736
yield keyword
in for expressions, 164, 513
Z
zip method
Prepared for Library Genesis
on class List, 352
on Iterable traits, 540
on trait Iterator, 590
zip method
on trait Future, 728
zipAll method
on Iterable traits, 540
on trait Iterator, 590
zipped method
on tuples, 367
zipWith method
on trait Future, 732
zipWithIndex method
on class List, 352
on Iterable traits, 540
on trait Iterator, 590
Cover · Overview · Contents · Discuss · Suggest · Glossary · Index
857
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Other titles from Artima Press
Property-based testing allows you to express
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Source Exif Data:
File Type : PDF File Type Extension : pdf MIME Type : application/pdf PDF Version : 1.5 Linearized : No Author : Bill Venners Create Date : 2016:04:06 12:58:01-07:00 Keywords : 2016, version=April 6, orderNumber=0B8GZ05FEi Modify Date : 2019:02:27 12:45:12+01:00 PTEX Fullbanner : This is pdfTeX, Version 3.14159265-2.6-1.40.16 (TeX Live 2015) kpathsea version 6.2.1 Producer : Aspose.Pdf for .NET 10.1.0 Trapped : False Description :Scala is an object-oriented programming language for the Java Virtual Machine. In addition to being object-oriented, Scala is also a functional language, and combines the best approaches to OO and functional programming.
.In Italian, Scala means a stairway, or steps. Indeed, Scala lets you step up to a programming environment that incorporates some of the best recent thinking in programming language design while also letting you use all your existing Java code.
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From the Inside Flap
.Programming in Scala, Third Edition, is the definitive book on Scala, the new language for the Java Platform that blends object-oriented and functional programming concepts into a unique and powerful tool for developers.Coauthored by the designer of the Scala language, this authoritative book will teach you, one step at a time, the Scala language and the ideas behind it.The book is carefully crafted to help you learn. The first few chapters will give you enough of the basics that you can already start using Scala for simple tasks. The entire book is organized so that each new concept builds on concepts that came before—a series of steps that promises to help you master the Scala language and the important ideas about programming that Scala embodies. A comprehensive tutorial and reference for Scala, this book covers the entire language and important libraries.
.From the Back Cover
.Programming in Scala Third Edition is the definitive book on Scala, the new language for the Java Platform that blends object-oriented and functional programming concepts into a unique and powerful tool for developers.
.Coauthored by the designer of the Scala language, this authoritative book will teach you, one step at a time, the Scala language and the ideas behind it.
.The book is carefully crafted to help you learn. The first few chapters will give you enough of the basics that you can already start using Scala for simple tasks. The entire book is organized so that each new concept builds on concepts that came before--a series of steps that promises to help you master the Scala language and the important ideas about programming that Scala embodies.
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.If I were to pick a language to use today other than Java, it would be Scala.
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.If you read the second edition...
.This third edition brings the entire book, up to date, adding new material to cover features appearing in Scala versions 2.9 through Scala 2.12, including:
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- .
- String interpolation .
- Functional Futures .
- Implicit classes .
- Defining new AnyVals .
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- The latest style recommendations .
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