Master techniques and concepts of functional programming to deliver safer, simpler, and more effective Kotlin code.
In Functional Programming in Kotlin you will learn:
• Functional programming techniques for real-world applications
• Write combinator libraries
• Common structures and idioms in functional design
• Simplicity and modularity (and fewer bugs!)
Functional Programming in Kotlin is a reworked version of the bestselling Functional Programming in Scala, with all code samples, instructions, and exercises translated into the powerful Kotlin language. In this authoritative guide, you’ll take on the challenge of learning functional programming from first principles. Complex concepts are demonstrated through exercises that you’ll love to test yourself against. You’ll start writing Kotlin code that’s easier to read, easier to reuse, better for concurrency, and less prone to bugs and errors.
About the technology
Improve performance, increase maintainability, and eliminate bugs! How? By programming the functional way. Kotlin provides strong support for functional programming, taking a pragmatic approach that integrates well with OO codebases. By applying the techniques you’ll learn in this book, your code will be safer, less prone to errors, and much easier to read and reuse.
About the book
Functional Programming in Kotlin teaches you how to design and write Kotlin applications using typed functional programming. Offering clear examples, carefully-presented explanations, and extensive exercises, it moves from basic subjects like types and data structures to advanced topics such as stream processing. This book is based on the bestseller Functional Programming in Scala by Rúnar Bjarnason and Paul Chiusano.
What's inside
• Functional programming techniques for real-world situations
• Common structures and idioms in functional design
• Simplicity, modularity, and fewer bugs!
About the reader
For Kotlin developers. No functional programming experience required.
About the author
Marco Vermeulen has two decades of programming experience on the JVM.
Rúnar Bjarnason and Paul Chiusano are the authors of Functional Programming in Scala.
Author(s): Marco Vermeulen, Rúnar Bjarnason, Paul Chiusano
Edition: 1
Publisher: Manning Publications
Year: 2021
Language: English
Commentary: Vector PDF
Pages: 504
City: Shelter Island, NY
Tags: Data Structures; Functional Programming; Parallel Programming; Stream Processing; Laziness; Monoids; Monads; Functors; Error Handling; Testing; Kotlin; Property-Based Testing; Higher-Order Functions; Strictness; Pure Functions; External Effects
Functional Programming in Kotlin
contents
foreword
preface
acknowledgments
about this book
Who should read this book
How this book is organized
How to read this book
About the code
liveBook discussion forum
Part 1—Introduction to functional programming
1 What is functional programming?
1.1 The benefits of FP: A simple example
1.1.1 A program with side effects
1.1.2 A functional solution: Removing the side effects
1.2 Exactly what is a (pure) function?
1.3 RT, purity, and the substitution model
1.4 What lies ahead
Summary
2 Getting started with functional programming in Kotlin
2.1 Higher-order functions: Passing functions to functions
2.1.1 A short detour: Writing loops functionally
2.1.2 Writing our first higher-order function
2.2 Polymorphic functions: Abstracting over types
2.2.1 An example of a polymorphic function
2.2.2 Calling HOFs with anonymous functions
2.3 Following types to implementations
Summary
3 Functional data structures
3.1 Defining functional data structures
3.2 Working with functional data structures
3.2.1 The “when” construct for matching by type
3.2.2 The when construct as an alternative to if-else logic
3.2.3 Pattern matching and how it differs from Kotlin matching
3.3 Data sharing in functional data structures
3.3.1 The efficiency of data sharing
3.4 Recursion over lists and generalizing to HOFs
3.4.1 More functions for working with lists
3.4.2 Lists in the Kotlin standard library
3.4.3 Inefficiency of assembling list functions from simpler components
3.5 Trees
Summary
4 Handling errors without exceptions
4.1 The problems with throwing exceptions
4.2 Problematic alternatives to exceptions
4.2.1 Sentinel value
4.2.2 Supplied default value
4.3 Encoding success conditions with Option
4.3.1 Usage patterns for Option
4.3.2 Option composition, lifting, and wrapping exception-oriented APIs
4.3.3 For-comprehensions with Option
4.4 Encoding success and failure conditions with Either
4.4.1 For-comprehensions with Either
Summary
5 Strictness and laziness
5.1 Strict and non-strict functions
5.2 An extended example: Lazy lists
5.2.1 Memoizing streams and avoiding recomputation
5.2.2 Helper functions for inspecting streams
5.3 Separating program description from evaluation
5.4 Producing infinite data streams through corecursive functions
5.5 Conclusion
Summary
6 Purely functional state
6.1 Generating random numbers using side effects
6.2 Purely functional random number generation
6.3 Making stateful APIs pure
6.4 An implicit approach to passing state actions
6.4.1 More power by combining state actions
6.4.2 Recursive retries through nested state actions
6.4.3 Applying the combinator API to the initial example
6.5 A general state action data type
6.6 Purely functional imperative programming
6.7 Conclusion
Summary
Part 2—Functional design and combinator libraries
7 Purely functional parallelism
7.1 Choosing data types and functions
7.1.1 A data type for parallel computations
7.1.2 Combining parallel computations to ensure concurrency
7.1.3 Marking computations to be forked explicitly
7.2 Picking a representation
7.3 Refining the API with the end user in mind
7.4 Reasoning about the API in terms of algebraic equations
7.4.1 The law of mapping
7.4.2 The law of forking
7.4.3 Using actors for a non-blocking implementation
7.5 Refining combinators to their most general form
Summary
8 Property-based testing
8.1 A brief tour of property-based testing
8.2 Choosing data types and functions
8.2.1 Gathering initial snippets for a possible API
8.2.2 Exploring the meaning and API of properties
8.2.3 Discovering the meaning and API of generators
8.2.4 Generators that depend on generated values
8.2.5 Refining the property data type
8.3 Test case minimization
8.4 Using the library and improving the user experience
8.4.1 Some simple examples
8.4.2 Writing a test suite for parallel computations
8.5 Generating higher-order functions and other possibilities
8.6 The laws of generators
8.7 Conclusion
Summary
9 Parser combinators
9.1 Designing an algebra
9.1.1 A parser to recognize single characters
9.1.2 A parser to recognize entire strings
9.1.3 A parser to recognize repetition
9.2 One possible approach to designing an algebra
9.2.1 Counting character repetition
9.2.2 Slicing and nonempty repetition
9.3 Handling context sensitivity
9.4 Writing a JSON parser
9.4.1 Defining expectations of a JSON parser
9.4.2 Reviewing the JSON format
9.4.3 A JSON parser
9.5 Surfacing errors through reporting
9.5.1 First attempt at representing errors
9.5.2 Accumulating errors through error nesting
9.5.3 Controlling branching and backtracking
9.6 Implementing the algebra
9.6.1 Building up the algebra implementation gradually
9.6.2 Sequencing parsers after each other
9.6.3 Capturing error messages through labeling parsers
9.6.4 Recovering from error conditions and backtracking over them
9.6.5 Propagating state through context-sensitive parsers
9.7 Conclusion
Summary
Part 3—Common structures in functional design
10 Monoids
10.1 What is a monoid?
10.2 Folding lists with monoids
10.3 Associativity and parallelism
10.4 Example: Parallel parsing
10.5 Foldable data structures
10.6 Composing monoids
10.6.1 Assembling more complex monoids
10.6.2 Using composed monoids to fuse traversals
Summary
11 Monads and functors
11.1 Functors
11.1.1 Defining the functor by generalizing the map function
11.1.2 The importance of laws and their relation to the functor
11.2 Monads: Generalizing the flatMap and unit functions
11.2.1 Introducing the Monad interface
11.3 Monadic combinators
11.4 Monad laws
11.4.1 The associative law
11.4.2 Proving the associative law for a specific monad
11.4.3 The left and right identity laws
11.5 Just what is a monad?
11.5.1 The identity monad
11.5.2 The State monad and partial type application
Summary
12 Applicative and traversable functors
12.1 Generalizing monads for reusability
12.2 Applicatives as an alternative abstraction to the monad
12.3 The difference between monads and applicative functors
12.3.1 The Option applicative vs. the Option monad
12.3.2 The Parser applicative vs. the Parser monad
12.4 The advantages of applicative functors
12.4.1 Not all applicative functors are monads
12.5 Reasoning about programs through the applicative laws
12.5.1 Laws of left and right identity
12.5.2 Law of associativity
12.5.3 Law of naturality
12.6 Abstracting traverse and sequence using traversable functors
12.7 Using Traversable to iteratively transform higher kinds
12.7.1 From monoids to applicative functors
12.7.2 Traversing collections while propagating state actions
12.7.3 Combining traversable structures
12.7.4 Traversal fusion for single pass efficiency
12.7.5 Simultaneous traversal of nested traversable structures
12.7.6 Pitfalls and workarounds for monad composition
Summary
Part 4—Effects and I/O
13 External effects and I/O
13.1 Factoring effects out of an effectful program
13.2 Introducing the IO type to separate effectful code
13.2.1 Handling input effects
13.2.2 Benefits and drawbacks of the simple IO type
13.3 Avoiding stack overflow errors by reification and trampolining
13.3.1 Reifying control flow as data constructors
13.3.2 Trampolining: A general solution to stack overflow
13.4 A more nuanced IO type
13.4.1 Reasonably priced monads
13.4.2 A monad that supports only console I/O
13.4.3 Testing console I/O by using pure interpreters
13.5 Non-blocking and asynchronous I/O
13.6 A general-purpose IO type
13.6.1 The main program at the end of the universe
13.7 Why the IO type is insufficient for streaming I/O
Summary
14 Local effects and mutable state
14.1 State mutation is legal in pure functional code
14.2 A data type to enforce scoping of side effects
14.2.1 A domain-specific language for scoped mutation
14.2.2 An algebra of mutable references
14.2.3 Running mutable state actions
14.2.4 The mutable array represented as a data type for the ST monad
14.2.5 A purely functional in-place quicksort
14.3 Purity is contextual
14.3.1 Definition by example
14.3.2 What counts as a side effect?
Summary
15 Stream processing and incremental I/O
15.1 Problems with imperative I/O: An example
15.2 Transforming streams with simple transducers
15.2.1 Combinators for building stream transducers
15.2.2 Combining multiple transducers by appending and composing
15.2.3 Stream transducers for file processing
15.3 An extensible process type for protocol parameterization
15.3.1 Sources for stream emission
15.3.2 Ensuring resource safety in stream transducers
15.3.3 Applying transducers to a single-input stream
15.3.4 Multiple input streams
15.3.5 Sinks for output processing
15.3.6 Hiding effects in effectful channels
15.3.7 Dynamic resource allocation
15.4 Application of stream transducers in the real world
Summary
Final words
Appendix A—Exercise hints and tips
A.1 Chapter 3: Functional data structures
Exercise 3.1
Exercise 3.2
Exercise 3.3
Exercise 3.4
Exercise 3.5
Exercise 3.6
Exercise 3.7
Exercise 3.12
Exercise 3.14
Exercise 3.15
Exercise 3.16
Exercise 3.17
Exercise 3.18
Exercise 3.19
Exercise 3.23
Exercise 3.28
A.2 Chapter 4: Handling errors without exceptions
Exercise 4.3
Exercise 4.4
Exercise 4.5
Exercise 4.6
Exercise 4.7
Exercise 4.8
A.3 Chapter 5: Strictness and laziness
Exercise 5.1
Exercise 5.2
Exercise 5.4
Exercise 5.6
Exercise 5.9
Exercise 5.10
Exercise 5.11
Exercise 5.14
Exercise 5.15
Exercise 5.16
A.4 Chapter 6: Purely functional state
Exercise 6.2
Exercise 6.5
Exercise 6.6
Exercise 6.7
Exercise 6.8
Exercise 6.9
Exercise 6.10
A.5 Chapter 7: Purely functional parallelism
Exercise 7.1
Exercise 7.2
Exercise 7.3
Exercise 7.5
Exercise 7.7
A.6 Chapter 8: Property-based testing
Exercise 8.4
Exercise 8.5
Exercise 8.6
Exercise 8.9
Exercise 8.12
Exercise 8.13
Exercise 8.16
A.7 Chapter 9: Parser combinators
Exercise 9.1
Exercise 9.2
Exercise 9.7
Exercise 9.9
Exercise 9.10
Exercise 9.12
A.8 Chapter 10: Monoids
Exercise 10.2
Exercise 10.3
Exercise 10.4
Exercise 10.5
Exercise 10.6
Exercise 10.7
Exercise 10.8
Exercise 10.9
Exercise 10.13
Exercise 10.19
A.9 Chapter 11: Monads and functors
Exercise 11.1
Exercise 11.2
Exercise 11.3
Exercise 11.4
Exercise 11.6
Exercise 11.7
Exercise 11.8
Exercise 11.9
Exercise 11.10
Exercise 11.13
Exercise 11.16
Exercise 11.18
Exercise 11.19
A.10 Chapter 12: Applicative and traversable functors
Exercise 12.2
Exercise 12.3
Exercise 12.5
Exercise 12.6
Exercise 12.7
Exercise 12.8
Exercise 12.9
Exercise 12.10
Exercise 12.11
Exercise 12.12
Exercise 12.13
Exercise 12.15
Exercise 12.16
Exercise 12.17
Exercise 12.18
Exercise 12.19
A.11 Chapter 13: External effects and I/O
Exercise 13.1
Exercise 13.2
Exercise 13.4
A.12 Chapter 14: Local effects and mutable state
Exercise 14.1
Exercise 14.2
Exercise 14.3
A.13 Chapter 15: Stream processing and incremental I/O
Exercise 15.3
Exercise 15.5
Exercise 15.6
Exercise 15.7
Exercise 15.9
Exercise 15.10
Exercise 15.11
Exercise 15.12
Appendix B—Exercise solutions
B.1 Before you proceed to the solutions
B.2 Getting started with functional programming
Exercise 2.1
Exercise 2.2
Exercise 2.3
Exercise 2.4
Exercise 2.5
B.3 Functional data structures
Exercise 3.1
Exercise 3.2
Exercise 3.3
Exercise 3.4
Exercise 3.5
Exercise 3.6
Exercise 3.7
Exercise 3.8
Exercise 3.9
Exercise 3.10
Exercise 3.11
Exercise 3.12 (Hard)
Exercise 3.13
Exercise 3.14 (Hard)
Exercise 3.15
Exercise 3.16
Exercise 3.17
Exercise 3.18
Exercise 3.19
Exercise 3.20
Exercise 3.21
Exercise 3.22
Exercise 3.23
Exercise 3.24
Exercise 3.25
Exercise 3.26
Exercise 3.27
Exercise 3.28
B.4 Handling errors without exceptions
Exercise 4.1
Exercise 4.2
Exercise 4.3
Exercise 4.4
Exercise 4.5
Exercise 4.6
Exercise 4.7
Exercise 4.8
B.5 Strictness and laziness
Exercise 5.1
Exercise 5.2
Exercise 5.3
Exercise 5.4
Exercise 5.5
Exercise 5.6 (Hard)
Exercise 5.7
Exercise 5.8
Exercise 5.9
Exercise 5.10
Exercise 5.11
Exercise 5.12
Exercise 5.13
Exercise 5.14 (Hard)
Exercise 5.15
Exercise 5.16 (Hard)
B.6 Purely functional state
Exercise 6.1
Exercise 6.2
Exercise 6.3
Exercise 6.4
Exercise 6.5
Exercise 6.6
Exercise 6.7
Exercise 6.8
Exercise 6.9
Exercise 6.10
Exercise 6.11
B.7 Purely functional parallelism
Exercise 7.1
Exercise 7.2
Exercise 7.3
Exercise 7.4
Exercise 7.5
Exercise 7.6
Exercise 7.7 (Hard)
Exercise 7.8 (Hard)
Exercise 7.9 (Hard/Optional)
Exercise 7.10
Exercise 7.11
Exercise 7.12
Exercise 7.13
B.8 Property-based testing
Exercise 8.1
Exercise 8.2
Exercise 8.3
Exercise 8.4
Exercise 8.5
Exercise 8.6
Exercise 8.7
Exercise 8.8
Exercise 8.9
Exercise 8.10
Exercise 8.11
Exercise 8.12
Exercise 8.13
Exercise 8.14
Exercise 8.15
Exercise 8.16
Exercise 8.17
B.9 Parser combinators
Exercise 9.1
Exercise 9.2 (Hard)
Exercise 9.3
Exercise 9.4
Exercise 9.5
Exercise 9.6
Exercise 9.7
Exercise 9.8
Exercise 9.9 (Hard)
Exercise 9.10
Exercise 9.11
Exercise 9.12
Exercise 9.13
Exercise 9.14
B.10 Monoids
Exercise 10.1
Exercise 10.2
Exercise 10.3
Exercise 10.4
Exercise 10.5
Exercise 10.6
Exercise 10.7
Exercise 10.8 (Hard/Optional)
Exercise 10.9 (Hard/Optional)
Exercise 10.10
Exercise 10.11
Exercise 10.12
Exercise 10.13
Exercise 10.14
Exercise 10.15
Exercise 10.16
Exercise 10.17
Exercise 10.18
Exercise 10.19
B.11 Monads and functors
Exercise 11.1
Exercise 11.2
Exercise 11.3
Exercise 11.4
Exercise 11.5
Exercise 11.6 (Hard)
Exercise 11.7
Exercise 11.8 (Hard)
Exercise 11.9
Exercise 11.10
Exercise 11.11
Exercise 11.12
Exercise 11.13 (Hard/Optional)
Exercise 11.14 (Hard/Optional)
Exercise 11.15 (Hard/Optional)
Exercise 11.16
Exercise 11.17
Exercise 11.18
Exercise 11.19 (Hard)
B.12 Applicative and traversable functors
Exercise 12.1
Exercise 12.2 (Hard)
Exercise 12.3
Exercise 12.4
Exercise 12.5
Exercise 12.6
Exercise 12.7 (Hard)
Exercise 12.8
Exercise 12.9
Exercise 12.10
Exercise 12.11
Exercise 12.12 (Hard)
Exercise 12.13 (Hard)
Exercise 12.14
Exercise 12.15
Exercise 12.16
Exercise 12.17
Exercise 12.18 (Hard)
Exercise 12.19 (Hard/Optional)
B.13 External effects and I/O
Exercise 13.1
Exercise 13.2
Exercise 13.3 (Hard)
Exercise 13.4 (Hard/Optional)
B.14 Local effects and mutable state
Exercise 14.1
Exercise 14.2
Exercise 14.3
B.15 Stream processing and incremental I/O
Exercise 15.1
Exercise 15.2
Exercise 15.3
Exercise 15.4
Exercise 15.5 (Hard)
Exercise 15.6
Exercise 15.7 (Optional)
Exercise 15.8
Exercise 15.9 (Optional)
Exercise 15.10
Exercise 15.11
Exercise 15.12
Appendix C—Higher-kinded types
C.1 A compiler workaround
C.2 Partially applied type constructors
C.3 Boilerplate code generation with Arrow Meta
Appendix D—Type classes
D.1 Polymorphism
D.2 Using type classes to express ad hoc polymorphism
D.3 Type classes foster a separation of concerns
index
Symbols
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
Y
Z
