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Leads to deep insights into the nature of computation.
Functional Programming in Scala is a serious tutorial for programmers looking to learn FP and apply it to the everyday business of coding. The book guides readers from basic techniques to advanced topics in a logical, concise, and clear progression. In it, you'll find concrete examples and exercises that open up the world of functional programming.
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About the Technology
Functional programming (FP) is a style of software development emphasizing functions that don't depend on program state. Functional code is easier to test and reuse, simpler to parallelize, and less prone to bugs than other code. Scala is an emerging JVM language that offers strong support for FP. Its familiar syntax and transparent interoperability with Java make Scala a great place to start learning FP.
Table of Contents takes you straight to the bookdetailed table of contents
foreword
preface
acknowledgments
about this book
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. Referential transparency, purity, and the substitution model
1.4. Summary
2. Getting started with functional programming in Scala
2.1. Introducing Scala the language: an example
2.2. Running our program
2.3. Modules, objects, and namespaces
2.4. Higher-order functions: passing functions to functions
2.4.1. A short detour: writing loops functionally
2.4.2. Writing our first higher-order function
2.5. Polymorphic functions: abstracting over types
2.5.1. An example of a polymorphic function
2.5.2. Calling HOFs with anonymous functions
2.6. Following types to implementations
2.7. Summary
3. Functional data structures
3.1. Defining functional data structures
3.2. Pattern matching
3.3. Data sharing in functional data structures
3.3.1. The efficiency of data sharing
3.3.2. Improving type inference for higher-order functions
3.4. Recursion over lists and generalizing to higher-order functions
3.4.1. More functions for working with lists
3.4.2. Loss of efficiency when assembling list functions from simpler components
3.5. Trees
3.6. Summary
4. Handling errors without exceptions
4.1. The good and bad aspects of exceptions
4.2. Possible alternatives to exceptions
4.3. The Option data type
4.3.1. Usage patterns for Option
4.3.2. Option composition, lifting, and wrapping exception-oriented APIs
4.4. The Either data type
4.5. 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. Infinite streams and corecursion
5.5. 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. A better API for state actions
6.4.1. Combining state actions
6.4.2. Nesting state actions
6.5. A general state action data type
6.6. Purely functional imperative programming
6.7. 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
7.1.3. Explicit forking
7.2. Picking a representation
7.3. Refining the API
7.4. The algebra of an API
7.4.1. The law of mapping
7.4.2. The law of forking
7.4.3. Breaking the law: a subtle bug
7.4.4. A fully non-blocking Par implementation using actors
7.5. Refining combinators to their most general form
7.6. Summary
8. Property-based testing
8.1. A brief tour of property-based testing
8.2. Choosing data types and functions
8.2.1. Initial snippets of an API
8.2.2. The meaning and API of properties
8.2.3. The meaning and API of generators
8.2.4. Generators that depend on generated values
8.2.5. Refining the Prop data type
8.3. Test case minimization
8.4. Using the library and improving its usability
8.4.1. Some simple examples
8.4.2. Writing a test suite for parallel computations
8.5. Testing higher-order functions and future directions
8.6. The laws of generators
8.7. Summary
9. Parser combinators
9.1. Designing an algebra, first
9.2. A possible algebra
9.2.1. Slicing and nonempty repetition
9.3. Handling context sensitivity
9.4. Writing a JSON parser
9.4.1. The JSON format
9.4.2. A JSON parser
9.5. Error reporting
9.5.1. A possible design
9.5.2. Error nesting
9.5.3. Controlling branching and backtracking
9.6. Implementing the algebra
9.6.1. One possible implementation
9.6.2. Sequencing parsers
9.6.3. Labeling parsers
9.6.4. Failover and backtracking
9.6.5. Context-sensitive parsing
9.7. 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
10.7. Summary
11. Monads
11.1. Functors: generalizing the map function
11.1.1. Functor laws
11.2. Monads: generalizing the flatMap and unit functions
11.2.1. The Monad trait
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 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
11.6. Summary
12. Applicative and traversable functors
12.1. Generalizing monads
12.2. The Applicative trait
12.3. The difference between monads and applicative functors
12.3.1. The Option applicative versus the Option monad
12.3.2. The Parser applicative versus the Parser monad
12.4. The advantages of applicative functors
12.4.1. Not all applicative functors are monads
12.5. The applicative laws
12.5.1. Left and right identity
12.5.2. Associativity
12.5.3. Naturality of product
12.6. Traversable functors
12.7. Uses of Traverse
12.7.1. From monoids to applicative functors
12.7.2. Traversals with State
12.7.3. Combining traversable structures
12.7.4. Traversal fusion
12.7.5. Nested traversals
12.7.6. Monad composition
12.8. Summary
Part 4 Effects and I/O
13. External effects and I/O
13.1. Factoring effects
13.2. A simple IO type
13.2.1. Handling input effects
13.2.2. Benefits and drawbacks of the simple IO type
13.3. Avoiding the StackOverflowError
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. 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
13.8. Summary
14. Local effects and mutable state
14.1. Purely functional mutable state
14.2. A data type to enforce scoping of side effects
14.2.1. A little language for scoped mutation
14.2.2. An algebra of mutable references
14.2.3. Running mutable state actions
14.2.4. Mutable arrays
14.2.5. A purely functional in-place quicksort
14.3. Purity is contextual
14.3.1. What counts as a side effect?
14.4. Summary
15. Stream processing and incremental I/O
15.1. Problems with imperative I/O: an example
15.2. Simple stream transducers
15.2.1. Creating processes
15.2.2. Composing and appending processes
15.2.3. Processing files
15.3. An extensible process type
15.3.1. Sources
15.3.2. Ensuring resource safety
15.3.3. Single-input processes
15.3.4. Multiple input streams
15.3.5. Sinks
15.3.6. Effectful channels
15.3.7. Dynamic resource allocation
15.4. Applications
15.5. Summary
index
What's inside
- Functional programming concepts
- The whys and hows of FP
- How to write multicore programs
- Exercises and checks for understanding
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