Book Image

Expert Python Programming - Fourth Edition

By : Michał Jaworski, Tarek Ziadé
Book Image

Expert Python Programming - Fourth Edition

By: Michał Jaworski, Tarek Ziadé

Overview of this book

This new edition of Expert Python Programming provides you with a thorough understanding of the process of building and maintaining Python apps. Complete with best practices, useful tools, and standards implemented by professional Python developers, this fourth edition has been extensively updated. Throughout this book, you’ll get acquainted with the latest Python improvements, syntax elements, and interesting tools to boost your development efficiency. The initial few chapters will allow experienced programmers coming from different languages to transition to the Python ecosystem. You will explore common software design patterns and various programming methodologies, such as event-driven programming, concurrency, and metaprogramming. You will also go through complex code examples and try to solve meaningful problems by bridging Python with C and C++, writing extensions that benefit from the strengths of multiple languages. Finally, you will understand the complete lifetime of any application after it goes live, including packaging and testing automation. By the end of this book, you will have gained actionable Python programming insights that will help you effectively solve challenging problems.
Table of Contents (16 chapters)
14
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15
Index

Summary

We've discussed the three main observability techniques of modern applications: logging, metrics collection, and distributed tracing. All of them have their advantages, but logging is definitely the most important way of collecting information from your application. That's because it is simple, does not require any special infrastructure (although it is good to have one), and is least likely to fail you.

But logging has some limitations. Unstructured logging messages make it harder to extract insights from logs. Logging is also not suitable for the periodic probing of information about resource usage and performance. It is good for auditing purposes and post-mortem analysis after major failures but is rarely helpful in tracking current information and reacting to sudden events.

That's why metrics collection systems are a natural and valuable extension of logging infrastructures. They allow you to collect information in real time, create custom metrics...