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Learning Functional Programming in Go

By : Sheehan

4.1 (8)

Learning Functional Programming in Go

4.1 (8)

By: Sheehan

Overview of this book

Lex Sheehan begins slowly, using easy-to-understand illustrations and working Go code to teach core functional programming (FP) principles such as referential transparency, laziness, recursion, currying, and chaining continuations. This book is a tutorial for programmers looking to learn FP and apply it to write better code. Lex guides readers from basic techniques to advanced topics in a logical, concise, and clear progression. The book is divided into four modules. The first module explains the functional style of programming: pure functional programming, manipulating collections, and using higher-order functions. In the second module, you will learn design patterns that you can use to build FP-style applications. In the next module, you will learn FP techniques that you can use to improve your API signatures, increase performance, and build better cloud-native applications. The last module covers Category Theory, Functors, Monoids, Monads, Type classes and Generics. By the end of the book, you will be adept at building applications the FP way.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

Pure Functional Programming in Go

Pure Functional Programming in Go

Motivation for using FP

Getting the source code

Imperative versus declarative programming

Pure functions

Fibonacci sequence - a simple recursion and two performance improvements

The difference between an anonymous function and a closure

Testing FP using test-driven development

A journey from imperative programming to pure FP and enlightenment

Summary

Manipulating Collections

Manipulating Collections

Iterating through a collection

Piping Bash commands

Functors

Predicates

Map and filter

Contains

If Go had generics

Itertools

Functional packages

Another time of reflection

The cure

Summary

Using High-Order Functions

Using High-Order Functions

Characteristics of FP

Sample HOF application

Summary

SOLID Design in Go

SOLID Design in Go

Why many Gophers eschew Java

Software design methodology

SOLID design principles

The big reveal

Viva La Duck

Summary

Adding Functionality with Decoration

Adding Functionality with Decoration

Interface composition

Decorator pattern

A decorator implementation

Summary

Applying FP at the Architectural Level

Applying FP at the Architectural Level

Application architectures

The role of systems engineering

Managing Complexity

FP influenced architectures

Domain Driven Design

Domain Driven Design

A cloud bucket application

FP and Micyoservices

Summary

Functional Parameters

Functional Parameters

Refactoring long parameter lists

Functional parameters

Contexts

Summary

Increasing Performance Using Pipelining

Increasing Performance Using Pipelining

Introducing the pipeline pattern

Example implementations

Summary

Functors, Monoids, and Generics

Functors, Monoids, and Generics

Understanding functors

Solve lack of generics with metaprogramming

Generics code generation tool

Generics implementation options

The shape of a functor

Composition operation

Functional composition in the context of a legal obligation

Build a 12-hour clock functor

The car functor

Monoids

Monoid examples

Summary

Monads, Type Classes, and Generics

Monads, Type Classes, and Generics

Mother Teresa Monad

Monadic workflow implementation

Y-Combinator

An alternative workflow option

Business use case scenarios

Y-Combinator re-examined

Type classes

Generics revisited

Summary

Category Theory That Applies

Category Theory That Applies

Our goal

Proof theory

The Curry Howard isomorphism

Historical Events in Functional Programming

Programming language categories

The Lambda Calculus

The importance of Type systems to FP

Domains, codomains, and morphisms

Set theory symbols

Category theory

Morphisms

Homomorphism

Composable concurrency

Graph Database Example

Using mathematics and category theory to gain understanding

Fun with Sums, Products, Exponents and Types

Big data, knowledge-driven development, and data visualization

Summary

Miscellaneous Information and How-Tos

Miscellaneous Information and How-Tos

How to build and run Go projects

Development workflow summary

A note about Go Dependency Management

A conversation - Java developer, idiomatic Go developer, FP developer

How to propose changes to Go

FP resources

Minggatu - Catalan number

Predicates

We can use predicates to perform operations on input data. Predicates can be used to implement many of the functions that we apply to collections to transform input data into the result collection or value.

Note

Thepredicate function is a function that takes one item as input and returns either true or false, based on whether the item satisfies some condition. They are often used conditionally to determine whether to apply certain operations in the execution chain.

Let's create some predicate functions that we can use to manipulate a collection of cars.

The All() function returns true only if all the values in the collection satisfy the predicate condition:

package predicate

func All(vals []string, predicate func(string) bool) bool {
for _, val := range vals {
if !predicate(val) {
return false
              }
       }
return true
}

The Any() function returns true as long as any one of the values in the collection satisfies the predicate condition:

funcAny(vs []string, predicate func...

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