Book Image

Python Architecture Patterns

By : Jaime Buelta
Book Image

Python Architecture Patterns

By: Jaime Buelta

Overview of this book

Developing large-scale systems that continuously grow in scale and complexity requires a thorough understanding of how software projects should be implemented. Software developers, architects, and technical management teams rely on high-level software design patterns such as microservices architecture, event-driven architecture, and the strategic patterns prescribed by domain-driven design (DDD) to make their work easier. This book covers these proven architecture design patterns with a forward-looking approach to help Python developers manage application complexity—and get the most value out of their test suites. Starting with the initial stages of design, you will learn about the main blocks and mental flow to use at the start of a project. The book covers various architectural patterns like microservices, web services, and event-driven structures and how to choose the one best suited to your project. Establishing a foundation of required concepts, you will progress into development, debugging, and testing to produce high-quality code that is ready for deployment. You will learn about ongoing operations on how to continue the task after the system is deployed to end users, as the software development lifecycle is never finished. By the end of this Python book, you will have developed "architectural thinking": a different way of approaching software design, including making changes to ongoing systems.
Table of Contents (23 chapters)
2
Part I: Design
6
Part II: Architectural Patterns
12
Part III: Implementation
15
Part IV: Ongoing operations
21
Other Books You May Enjoy
22
Index

Queue effects

An important element of asynchronous tasks is the effect that introducing a queue may have. As we've seen, the background tasks are slow, meaning that any worker running them will be busy for some time.

Meanwhile, more tasks can be introduced, which may mean that the queue starts building up.

Figure 7.4: Single queue

On the one hand, this can be a capacity problem. If the number of workers is not sufficient to handle the average number of tasks introduced in the queue, the queue will build up until it reaches its limit, and new tasks will be rejected.

But typically, the load doesn't work like a constant influx of tasks. Instead, there are times when there are no tasks to execute, and other times when there's a sudden spike in the number of tasks to be executed, filling the queue. Also, there's a need to calculate the right number of workers to keep running to be sure that the waiting period for those spikes, where a task gets...