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Mastering Python Design Patterns

Mastering Python Design Patterns - Third Edition

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

Mastering Python Design Patterns

4.3 (8)
By: Kamon Ayeva, 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 Retry pattern

Retrying is an approach that is increasingly needed in the context of distributed systems. Think about microservices or cloud-based infrastructures where components collaborate with each other but are not developed or deployed/operated by the same teams and parties.

In its daily operation, parts of a cloud-native application may experience what are called transient faults or failures, meaning some mini-issues that can look like bugs but are not due to your application itself; rather, they are due to some constraints outside of your control such as the networking or the external server/service performance. As a result, your application may malfunction (at least, that could be the perception of your users) or even hang in some places. The answer to the risk of such failures is to put in place some retry logic so that we pass through the issue by calling the service again, maybe immediately or after some wait time (such as a few seconds).

Real-world examples

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Mastering Python Design Patterns
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