Book Image

Hands-On Design Patterns and Best Practices with Julia

By : Tom Kwong
Book Image

Hands-On Design Patterns and Best Practices with Julia

By: Tom Kwong

Overview of this book

Design patterns are fundamental techniques for developing reusable and maintainable code. They provide a set of proven solutions that allow developers to solve problems in software development quickly. This book will demonstrate how to leverage design patterns with real-world applications. Starting with an overview of design patterns and best practices in application design, you'll learn about some of the most fundamental Julia features such as modules, data types, functions/interfaces, and metaprogramming. You'll then get to grips with the modern Julia design patterns for building large-scale applications with a focus on performance, reusability, robustness, and maintainability. The book also covers anti-patterns and how to avoid common mistakes and pitfalls in development. You'll see how traditional object-oriented patterns can be implemented differently and more effectively in Julia. Finally, you'll explore various use cases and examples, such as how expert Julia developers use design patterns in their open source packages. By the end of this Julia programming book, you'll have learned methods to improve software design, extensibility, and reusability, and be able to use design patterns efficiently to overcome common challenges in software development.
Table of Contents (19 chapters)
1
Section 1: Getting Started with Design Patterns
3
Section 2: Julia Fundamentals
7
Section 3: Implementing Design Patterns
15
Section 4: Advanced Topics

Covariance, invariance, and contravariance

As it turns out, the rules for subtyping are not very straightforward. When you look at a simple type hierarchy, you can immediately tell whether one type is a subtype of another by tracing the relationships of the data types in the hierarchy. The situation becomes more complex when parametric types are involved. In this section, we will take a look at how Julia is designed with respect to variance, a concept that explains subtyping relationships for parametric types.

Let's first review the different kinds of variance.

Understanding different kinds of variance

There are four different kinds of variance as described in computer science literature. We will first describe them...