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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

Other distributed systems patterns

There are many more distributed systems patterns than the ones we covered here. Among the other patterns developers and architects can use are the following:

  • Command and Query Responsibility Segregation (CQRS): This pattern separates the responsibilities for reading and writing data, allowing for optimized data access and scalability by tailoring data models and operations to specific use cases
  • Two-Phase Commit: This distributed transaction protocol ensures atomicity and consistency across multiple participating resources by coordinating a two-phase commit process, involving a prepare phase followed by a commit phase
  • Saga: A saga is a sequence of local transactions that together form a distributed transaction, providing a compensating mechanism to maintain consistency in the face of partial failures or aborted transactions
  • Sidecar: The Sidecar pattern involves deploying additional helper services alongside primary services to enhance...

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