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Python Architecture Patterns

Python Architecture Patterns

By : Jaime Buelta
4.6 (22)
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Python Architecture Patterns

Python Architecture Patterns

4.6 (22)
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)
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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

Metrics versus logs

As we saw in the previous chapter, logs are text messages produced as code is executed. They are good at giving visibility on each of the specific tasks that the system is performing, but they generate a huge amount of data, which is difficult to digest in bulk. Instead, only small groups of logs are able to be analyzed at any given time.

Normally, the logs analyzed will all be related to a single task. We saw in the previous chapter how to use a request ID for that. But on certain occasions, it may be necessary to check all logs happening in a particular time window to see crossing effects, like a problem in one server that affects all tasks during certain times.

But sometimes the important information is not a specific request, but to understand the behavior of the system as a whole. Is the load of the system growing compared to yesterday's? How many errors are we returning? Is the time it takes to process tasks increasing? Or decreasing...

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