Grokking Functional Programming
Grokking Functional Programming is a practical book. Written especially for object-oriented programmers, it will help you map familiar ideas like objects and inheritance to FP concepts such as programming with immutable data, higher order functions, composing functions, concurrent programming and even the dreaded monad. And you'll be pleased to know that we skip the academic baggage of lambda calculus, category theory, and the mathematical foundations of FP in favor of applying functional programming to everyday programming tasks. At the end of the book, you'll be ready to pick a functional language and start writing useful software.
Table of Contents detailed table of contents
PART 1: RETHINKING PROGRAMMING
1. Learning Functional Programming
Perhaps you picked up this book because…
Don’t read this book if…
What do you need to know before we start?
What language are we going to use?
What this book expects of you
Where does functional programming fit?
Not just programming in a new language
Get our tools ready
What tools can we use?
A little functional fun
Abbreviating names explained
Coffee Break exercises explained
More fun with Strings
Fun with Strings explained
Summary
2. Referential Transparency
What is interesting about maths functions?
Why do we have such high trust in maths?
A review of the quick exercises
A second reason to trust maths: no side effects
What just happened to our classes?
Going closer to purity
Another Quick Exercise
Review of Exercise #3
Referential Transparency is why we trust maths
Steps to achieving referential transparency
One last exercise
Review of Exercise #4
From Java to Scala: does an FP language help us?
Defining Immutable objects with Scala
Experiencing Immutable objects with Scala
Experiments with Immutable collections
Using immutable collections in Scala
Aggregation over immutable collections
PAUSE: Where are we?
Where to from here?
Immutability
Persistent Data Structures
Higher Order Functions
Multi-threading and Concurrency
Summary
3. Programming with Immutable Collections
Programming with immutability is strange
Immutability at block scope
How do we add a list of numbers in our head?
Another way to think about looping
Implementing loops without loops in code
No loops: a recursive sum function in Scala
We still value expression of purpose with functions
A Scala way to hide our recursive implementation
Counting recursively
Using a local function for the recursive count
Practicing Recursion
Solutions to our recursion practice exercises
Nasty recursive functions
When can recursion cause a stack overflow?
How to cause a stack overflow in a few lines
Visualising the different recursion functions
Time to catch our breath
We still value DRY - Don’t Repeat Yourself
What shall we name our DRY recursive function?
Passing in code so that we still honor DRY
Passing code into foldLeft with Scala
Understanding Scala’s built-in foldLeft function
Playing with Scala’s foldLeft function
Playing with Scala’s foldLeft explained
Summary
4. Immutable Collections
Functions for programming immutable collections
How to add elements to a mutable list
How to add elements to an immutable list
Heads and Tails: Another way to think about lists
Some observations about immutable lists
A Simple linked list
Simple Linked List: Prepend or append?
Simple Linked List: Concatenating two lists
Coffee Break: Reverse a linked list
Coffee Break Explained: Reverse a linked list
Linked Lists: Summary
Another interesting data structure: binary search tree
Binary Search Tree: A Simple Scala Implementation
Binary Search Tree: Structural Sharing
Binary Search Tree: Inserting items
Binary Search Tree: A Path for Depth First Traversal
Coffee Break: Find the maximum value in a tree
Coffee Break Explained: Maximum value in a tree
Binary Search Tree: Summary
Why do we care about persistent data structures?
Transforming persistent data structures
Transforming with filter
Practicing with filter
Transforming with map
Practicing with map
Combining filters and maps into pipelines
Coffee Break: data transformations
Coffee Break Explained: data transformations
Summary
5. List Comprehensions
List Comprehensions: An alternative to map and filter
List Comprehensions with Scala: the for-expression
Coffee Break: Simple list comprehensions
Coffee Break Explained: Simple list comprehensions
A Generalisation of a list comprehension
Comprehension over lists of objects
Coffee Break: Working with lists of objects
Coffee Break Explained: Working with lists of objects
Comprehension over lists that have lists
Comprehensions on lists of lists are flatMaps
Relating filter, map and flatMap to comprehensions
Working with multiple lists
Coffee Break: Working with multiple lists
Coffee Break Explained: Working with multiple lists
List Comprehensions or filter, map and flatMap?
Summary
6. Immutable Data Structures
Our journey of learning to program with immutability
Boolean: A really simple immutable data structure
Coffee Break: Extend the SwitchState data structure
Coffee Break Explained: Extend the SwitchState
Recursive data structures
Implementing a tree as a sum type data structure
Coffee Break: Find the size of a tree
Coffee Break Explained: Finding the size of a tree
Data structures that are combinations of elements
Programming with immutable data structures
Thinking in Data Structures is thinking in Types
Coffee Break: More types for the Turtle
Coffee Break Explained: More types for the Turtle
Interesting applications of functional data structures
Category Theory and Algebraic Data Types
Summary
7. Software Transactional Memory
Why should we bother with higher order functions?
A look at concise code with higher order functions
Concise code tends to be declaritive code
What is a higher order function?
A higher order function receives functions as inputs
Coffee Break: functions that receive functions
Coffee Break: functions that receive functions
A higher order function can return a function as output
Binding input data to returned functions
Coffee Break: functions that return functions
Coffee Break: functions that return functions
Functions as Types
Designing with Functions and Data as Types
Wrap-up: Abstractions with Functions and Data Types
Coffee Break: Designing with Functions and Data
Coffee Break: Designing with Functions and Data
Summary
Binding data to functions
Combinators
Composition
Data stream programming
Concurrency with Actors
PART 2: APPLYING FUNCTIONAL
User Interface programming
Web application programming
Pure Simon Says
Modularity
About the Technology
Functional programming is more than just writing the same old code in Scala, Clojure, or Haskell. To grok FP—to really get it—you need to rewire your brain to see the world differently. We're here to help you flip the switch. Grokking Functional Programming teaches you first to break down problems in a new way so you can approach them from a FP mindset. Following carefully-selected examples with thorough, carefully-paced explanations, you'll immerse yourself in FP concept by concept. Along the way, exercises, checks for understanding, and even the occasional puzzler give you opportunities to think and practice what you're learning.
What's inside
- Designing with functions instead of objects
- Multiple learning approaches to help you grok each new concept
- A practical programming-first teaching style
- Programming with immutable collections
- Writing concurrent programs with a functional style
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