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Mastering Python Design Patterns

Mastering Python Design Patterns

By : Kamon Ayeva, Kasampalis, Sakis Kasampalis
4.3 (8)
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Mastering Python Design Patterns

Mastering Python Design Patterns

4.3 (8)
By: Kamon Ayeva, Kasampalis, Sakis Kasampalis

Overview of this book

As software systems become increasingly complex, maintaining code quality, scalability, and efficiency can be a daunting challenge. Mastering Python Design Patterns is an essential resource that equips you with the tools you need to overcome these hurdles and create robust, scalable applications. The book delves into design principles and patterns in Python, covering both classic and modern patterns, and apply them to solve daily challenges as a Python developer or architect. Co-authored by two Python experts with a combined experience of three decades, this new edition covers creational, structural, behavioral, and architectural patterns, including concurrency, asynchronous, and performance patterns. You'll find out how these patterns are relevant to various domains, such as event handling, concurrency, distributed systems, and testing. Whether you're working on user interfaces (UIs), web apps, APIs, data pipelines, or AI models, this book equips you with the knowledge to build robust and maintainable software. The book also presents Python anti-patterns, helping you avoid common pitfalls and ensuring your code remains clean and efficient. By the end of this book, you'll be able to confidently apply classic and modern Python design patterns to build robust, scalable applications.
Table of Contents (17 chapters)
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1
Part 1: Start with Principles
4
Part 2: From the Gang of Four
8
Part 3: Beyond the Gang of Four

The Cache-Aside pattern

In situations where data is more frequently read than updated, applications use a cache to optimize repeated access to information stored in a database or data store. In some systems, that type of caching mechanism is built in and works automatically. When this is not the case, we must implement it in the application ourselves, using a caching strategy that is suitable for the particular use case.

One such strategy is called Cache-Aside, where, to improve performance, we store frequently accessed data in a cache, reducing the need to fetch data from the data store repeatedly.

Real-world examples

We can cite the following examples in the software realm:

  • Memcached is commonly used as a cache server. It is a popular in-memory key-value store for small chunks of data from the results of database calls, API calls, or HTML page content.
  • Redis is another server solution that is used for cache. Nowadays, it is my go-to server for caching or application...

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