Throughout my career, I have primarily focused on developing backend systems using Python. Despite dabbling in front-end work, I always found myself gravitating back toward API development. In order to expand my skill set, I have experimented with writing backend applications using different programming languages such as JavaScript/Typescript and Rust. I have tested various technologies and frameworks, some of which I found more enjoyable than others. Nonetheless, there remains one language that I have yet to explore: Go. When I first started using Golang, it felt a little unfamiliar due to its simplicity, unique error handling, and some unusual design choices like struct tags. However, even before getting fully accustomed to these "quirks", I quickly realized several things about Go that I genuinely appreciate. Interestingly, these same features can be seen as challenges in my primary programming language, Python. In this article, I will share my initial thoughts on using Go as a Python engineer. I will highlight the aspects of Go that I appreciate, which may not be as prominent in Python. These include: built-in tools packaging backend focus setting up Docker

While Django and Flask remain the first choices for many Python engineers, FastAPI has already been recognized as an undeniably reliable pick. It is a highly flexible, well-optimized, structured framework that gives the developer endless possibilities for building backend applications. Working with databases is an essential aspect of most backend applications. As a result, the ORM plays a critical role in the backend code. However, unlike Django, FastAPI does not have an ORM built-in. It is entirely the developer's responsibility to select a suitable library and integrate it into the codebase. Python engineers widely consider SQLAlchemy to be the most popular ORM available. It's a legendary library that's been in use since 2006 and has been adopted by thousands of projects. In 2023, it received a major update to version 2.0. Similar to FastAPI, SQLAlchemy provides developers with powerful features and utilities without forcing them to use them in a specific way. Essentially, it's a versatile toolkit that empowers developers to use it however they see fit. FastAPI and SQLAlchemy are a match made in heaven. They are both reliable, performant, and modern technologies, which enable the creation of powerful and unique applications. This article explores creating a FastAPI backend application that utilizes SQLAlchemy 2.0 as the ORM. The content covers: building models using Mapped and mapped_column defining an abstract model

Hi, my name is Piotr. I am a software engineer from Poland, currently working full-time in Germany. About I have always been interested in computers and software. I started my programming adventure quite early, by learning Java and writing some simple Minecraft mods. The possibilities of simply writing a text in a code editor and watching some crazy things happening, amazed me. Upon entering college, I quickly realized that I was bored with just theory and simple exercises. As a result, I decided to learn Python and pursue a career in web development as soon as possible. I am absolutely passionate about all the work I do. I always strive to do my best, whether it's designing or implementing solutions. I simply enjoy building things in general. Additionally, I am eager to explore various concepts and share my knowledge with others. Creating content for my blog allows me to do both on a regular basis. Beside programming, I also like reading nonfiction and fantasy books, playing video games and practicing kickboxing.

Python offers a range of powerful features for object-oriented programming, including descriptors. These enable you to define how class attribute modification and access occur, making it useful for enforcing value constraints, implementing computed properties, and customizing attribute access. This article covers the fundamentals of descriptors and provides guidance on how to implement them in your Python code. It addresses the following questions: what is the definition of a descriptor how to create a descriptor from scratch how the descriptors actually work what can be built using descriptors Descriptors may sound unfamiliar to you, but they are widely used in popular packages such as Django and SQLAlchemy. This article will provide you with a clear understanding of descriptors and enable you to develop your own.

While Django and Flask remain the first choices for many Python engineers, FastAPI has already been recognized as an undeniably reliable pick. It is a highly flexible, well-optimized, structured framework that gives the developer endless possibilities for building backend applications. Working with databases is an essential aspect of most backend applications. As a result, the ORM plays a critical role in the backend code. However, unlike Django, FastAPI does not have an ORM built-in. It is entirely the developer's responsibility to select a suitable library and integrate it into the codebase. Python engineers widely consider SQLAlchemy to be the most popular ORM available. It's a legendary library that's been in use since 2006 and has been adopted by thousands of projects. In 2023, it received a major update to version 2.0. Similar to FastAPI, SQLAlchemy provides developers with powerful features and utilities without forcing them to use them in a specific way. Essentially, it's a versatile toolkit that empowers developers to use it however they see fit. FastAPI and SQLAlchemy are a match made in heaven. They are both reliable, performant, and modern technologies, which enable the creation of powerful and unique applications. This article explores creating a FastAPI backend application that utilizes SQLAlchemy 2.0 as the ORM. The content covers: building models using Mapped and mapped_column defining an abstract model

Python generators are a crucial feature with multiple uses, from lazy iteration to continuous data streaming. They are heavily adopted in popular packages such as FastAPI, SQLAlchemy, pytest, and others, highlighting their power and the importance of understanding how they work. This article provides answers to the following questions about generators: What are they exactly? How do they work? What are generator expressions and subgenerators? What are some common use cases for generators? Each section covers both theoretical and practical aspects of Python generators. By the end, you will have a strong comprehension of generators and their importance. The first section begins by defining what a generator is and how it operates.

Debugging is an essential part of software development. It can be a time-consuming and frustrating process. There is nothing wrong with putting print statements inside the code to check something quickly. If it works for you in most cases, then why should you worry about overcomplicating this process? However, in more complex scenarios, using just a logging mechanism may be insufficient or even unfeasible. This article covers the usage of Visual Studio Code debugging tools for Python applications. Examples are shown on the FastAPI app running on top of the uvicorn server. The article addresses the following scenarios: debugging a locally running code running a container and attaching a debugger to it attaching to a running container and debugging the code inside it After reading this article, you will have a comprehensive understanding of the different debugging methods available in VSCode. This will enable you to choose the most appropriate strategy for any given scenario.

One of the most interesting and useful features of Python are decorators, which are callable objects that modify the behavior of other pieces of code without any additional changes. Decorators are a fundamental concept in Python, and they are used extensively in the language's standard library, as well as in third-party libraries and frameworks. Some of the most common usages of decorators include: logging timing caching error handling Decorators are also a great way to create reusable code since they can be applied to multiple functions.

TUI stands for "text user interface". It refers to a type of user interface that relies primarily on text and symbols to interact with users, rather than visual elements like icons and images. TUI applications typically run in a command-line interface (CLI) environment, which is a type of interface where users interact with a computer by typing commands into a text-based interface. TUI applications have been around for a long time and despite their seemingly archaic design, they have certain advantages over graphical user interface (GUI) applications. These include faster response times, lower system requirements, and easier automation. Is It Really in the Terminal? Several libraries, such as urwid or PyTermGUI, allow the development of TUI applications in Python. For enhancing the functionality and aesthetics of TUI apps, they offer some fundamental and more sophisticated utilities. But there is one package that is truly exceptional and might even be so amazing that it sparks a TUI renaissance (I really wanted to put "TUI renaissance" somewhere in this article). Textual is a package created by Will McGugan, the creator of the rich, extremely popular terminal text formatting library. Textual is actually built on top of rich to support all of those lovely features, add interactivity to them, and enable the creation of more complicated apps and components. When you first encounter an example of Textual app (and you can find them directly in the Textual repository), you may wonder: is it really a built-in terminal? Yes, it is. It's no coincidence that Textual has reached approximately 20k GitHub stars in less than two years. You don't win the hearts of the community by just giving a tool that does something. In the case of Textual, it is obviously much more than that. First and foremost, the code quality is excellent; everything is type-hinted and documented. Another point is how practical and simple Textual is to use. This is because there are so many built-in features. Async support is also included right out of the box. Eventually, working with Textual produces effects that are simply remarkable. In conclusion, you may easily and quickly create the most stunning TUI apps that have ever been seen. ChatGPT and Textual

Saying "you must know it" in pretty much any field of science can be quite contentious. There is no exception in software development. You may frequently see tweets or posts stating things like "you must learn Blockchain if you don't want to be left behind" or "you must know Kubernetes because it is so popular right now." Spoiler: You don't. However, if you want to be an expert in your field, there might be some topics that are almost universal or are used so frequently that it can be challenging without at least a fundamental understanding. Python has gathered an incredibly strong community after over 30 years of its existence. As a result, Python has a plethora of well-known libraries and frameworks. Only by looking at the list of the most popular backend frameworks, you can see 2 Python libraries - Django and Flask. Right behind the corner, there is also FastAPI, which, given its popularity, will probably overtake Flask. With numerous excellent libraries available, there must be at least a few that are universal, or ubiquitous enough to qualify as "must-knows". Managing Linters with pre-commit Writing code is, of course, a major part of software development. Fortunately, formatting and style maintenance are not a major part of writing the code. There are numerous tools that lint the code to ensure that it appears consistently throughout the project, and determine whether the style guide is being followed. You've probably seen or used at least one of these libraries: black flake8 isort

It should not be innovative to say that writing software is not merely about writing code - it is about solving a particular problem. Even though it's developers who eventually implement the solution, it is not developers who define what is the problem in the first place. That task is carried out by various business people, who consider processes, risks, and outcomes to describe what the problem is, why it exists, and how it should be addressed. In a domain-driven context, these business people are referred to as Domain Experts. From an engineering perspective, it appears that Domain Experts hold a valuable asset: their knowledge about the domain. However, this knowledge is rarely shared in its raw form. Instead, it's usually translated into requirements so that developers can understand and implement it. The issue with this approach is that the domain knowledge of business people and developers can diverge. This means that the perspectives of those who define the problem and those who work on solving it may not align, leading to misunderstandings and conflicts. So, what's the way out? Make sure that business and technical people use the same language and terminology. What is Domain-Driven Design? Domain-Driven Design (DDD) is a methodology that emphasizes the importance of creating a shared understanding between domain experts and technical stakeholders and aligning the software solution with the underlying business requirements. This appears to be a high-level, non-technical definition, but it can also be broken down into something more developer-friendly: DDD is representing real-world concepts in the code, and its structure by cultivating and using ubiquitous language, that is built by modeling the business domain.