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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

Querying Prometheus

Prometheus has its own query system, called PromQL, and ways of operating with metrics that, while powerful, can be a little confusing at the beginning. Part of it is its pull approach to metrics.

For example, requesting one useful metric, like django_http_requests_latency_seconds_by_view_method_count, will display how many times each view has been called for each method.

Figure 13.6: Notice how the prometheus-django-metrics view is called more often, as it is called automatically by Prometheus once every 15 seconds to scrape the results

This is presented as an accumulated value that grows over time. This is not very useful, as it's difficult to make sense of what exactly it means.

Instead, the value is more likely to be presented as a rate, representing how many requests have been detected per second. For example, with a resolution of 1 minute, rate(django_http_requests_latency_seconds_by_view_method_count[1m]) shows the following graph...

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