Hynek Schlawack: Pythonista, Gopher, blogger, speaker, and YouTuber from Berlin / Germany.
Python appears to be everywhere nowadays! How did it happen, that a language that almost died in the Python 2 to 3 process is now the lingua franca a default choice when people talk about programming? There must be some secret superpower and I think I found it!
Ever had this weird gut feeling that something is off in your code, but couldn’t put the finger on why? Are you starting your projects with the best intentions, following all best practices, and still feel like your architecture turns weird eventually?
Whenever I publish something about my Python Docker workflows, I invariably get challenged about whether it makes sense to use virtual environments in Docker containers. As always, it’s a trade-off, and I err on the side of standards and predictability.
Starting with 0.3.0 , Astral’s uv brought many great features, including support for cross-platform lock files uv.lock . Together with subsequent fixes, it has become Python’s finest workflow tool for my (non-scientific) use cases. Here’s how I build production-ready containers, as fast as possible.
One of my first TIL entries was about how you can imitate Node’s node_modules semantics in Python on UNIX-like operating systems. A lot has happened since then (to the better!) and it’s time for an update. direnv still rocks, though.
When contributing to other users’ repositories, always start a new branch in your fork.
No, it’s not (just) run-parallel – let’s cut the local tox runtime by 75%!
Ever seen a code base where understanding a simple method meant jumping through tangled class hierarchies? We all have! And while “Favor composition over inheritance!” is almost as old as object-oriented programming, strictly avoiding all types of subclassing leads to verbose, un-Pythonic code. So, what to do?
Ever since I got involved with open-source Python projects, tox has been vital for testing packages across Python versions (and other factors). However, lately, I’ve been increasingly using Nox for my projects instead. Since I’ve been asked why repeatedly, I’ll sum up my thoughts.
Why does DYLD_LIBRARY_PATH keep disappearing!?
A love letter to tools that changed everything for me.
One of my (slowly evaporating) reasons why I like putting packaging metadata into an executable setup.py is the ability to have optional dependencies that are combinations of others. As of pip 21.2, this is possible without running code.
A common issue when writing tests for real-world software is how to deal with third-party dependencies. Let’s examine an old, but counter-intuitive principle.
An attempt at catharsis. This is a deeply personal blog post about the most influential project I’ve ever created: attrs , the progenitor of modern Python class utilities. I’m retelling its history from my perspective, how it begot dataclasses , and how I’m leading it into the future.
Codecov’s unreliability breaking CI on my open source projects has been a constant source of frustration for me for years. I have found a way to enforce coverage over a whole GitHub Actions build matrix that doesn’t rely on third-party services.
Trying out something new: today I’m launching my own Today I Learned section. In this essay I will sum up what my plans and hopes are.
The conflict between subclassing and composition is as old as object-oriented programming. The latest crop of languages like Go or Rust prove that you don’t need subclassing to successfully write code. But what’s a pragmatic approach to subclassing in Python, specifically?
This post is less about teaching and more about schadenfreude amusement for you, and catharsis for me. It’s the story of how one unfortunate HTML tag kicked me off almost all search engines and my months-long way back. And why it didn’t matter in the end.
The widely used Python package cryptography changed their build system to use Rust for low-level code, which caused an emotional GitHub thread. Enthusiasts of 32-bit hardware from the 1990s aside, a vocal faction stipulated adherence to Semantic Versioning from the maintainers, claiming it would’ve prevented all grief. I will show you not only why this is wrong but also how relying on Semantic…
Lie and lay are infamously confusing to non-native speakers. It’s so bad that it sparked a cottage industry of click-baity articles full of sketchy ads. Since English is my third language, I stumbled a lot myself until I wrote this cheatsheet.
One of the main appeals of using Python’s asyncio is being able to fire off many coroutines and run them concurrently. How many ways do you know for waiting for their results?
Let’s talk about abstractions and Pythonic code.
Some projects have the policy that all tests must have an explanatory comment – including all of mine. At first, I found that baffling. If that’s you right now, this article is for you.
GitHub’s own CI called GitHub Actions has been out of closed beta for a while and offers generous free quotas and a seamless integration with the rest of the site. Let’s have a look at how to use it for an open source Python package.
I’m missing a key part from the public Python discourse and I would like to help to change that.
Since this topic keeps coming up, I’d like to briefly share my thoughts on Python package metadata because it’s – as always – more complex than it seems.
People always ask for book recommendations, so here’s my list of books that changed my life in one way or another. None of them is related to tech or programming, but all affect how I think and work.
Since the acquisition of Travis CI , the future of their free offering is unclear. Azure Pipelines has a generous free tier, but the examples I found are discouragingly complex and take advantage of features like templating that most projects don’t need. To close that gap, this article shows you how to move a Python project with simple CI needs from Travis CI to Azure Pipelines.
There are many good reasons not to go to every talk possible when attending conferences. However, increasingly, it became hip to boast about avoiding going to talks – encouraging others to follow suit. That rubs me the wrong way as a speaker, and I’ll try to explain why.
PyPI is a gold mine of great packages but those packages have to be written first. More often than not, projects that millions of people depend on are written and maintained by only one person. If you’re unlucky, that person is you! This talk tries to lighten the burden by giving you useful tools and approaches.
We have more ways to manage dependencies in Python applications than ever. But how do they fare in production? Unfortunately this topic turned out to be quite polarizing and was at the center of a lot of heated debates. This is my attempt at an opinionated review through a DevOps lens.
The DevOps movement gave us many ways to put Python applications into production. But how can you practically structure and configure your applications to make them indifferent to the environment they run in? How do secrets fit into the picture? And where do you put that log file?
Most Python programmers don’t spend a lot of time thinking about how equality and hashing works. It usually just works. However there’s quite a bit of gotchas and edge cases that can lead to subtle and frustrating bugs once one starts to customize their behavior – especially if the rules on how they interact aren’t understood.
Proper cleanup when terminating your application isn’t less important when it’s running inside of a Docker container. Although it only comes down to making sure signals reach your application and handling them, there’s a bunch of things that can go wrong.
I’ve seen quite a bit of the world thanks to being invited to speak at conferences. Since some people are under the impression that serial conference speakers possess some special talents, I’d like to demystify my process by walking you through my latest talk from start to finish.
No matter whether you run a web app, search for gravitational waves, or maintain a backup script: reliability of your systems make the difference between sweet dreams and production nightmares at 4am.
The Python standard library is full of underappreciated gems. One of them allows for simple and elegant function dispatching based on argument types . This makes it perfect for serialization of arbitrary objects – for example to JSON in web APIs and structured logs.
If your Python decorator unintentionally changes the signatures of my callables or doesn’t work with class methods, it’s broken and should be fixed. Sadly most decorators are broken because the web is full of bad advice.
To get real time insight into running applications you need to instrument them and collect metrics: count events, measure times, expose numbers. That used to be a clusterfuck of technologies and approaches. Prometheus changes that.
Since the inception of wheels that install Python packages without executing arbitrary code, we need a static way to encode conditional dependencies for our packages. Thanks to PEP 508 we do have a blessed way but sadly the prevalence of old setuptools and pip versions make it a minefield to use.
My completely anecdotal view on the state of Python 3 in 2016. Based on my own recent experience, observations, and exchanges with other members of the Python community.
Don’t use Python’s hasattr() unless you’re writing Python 3- only code and understand how it works.
Why “Use bcrypt .” is not the best recommendation (anymore).
How to ensure that your tests run code that you think they are running, and how to measure your coverage over multiple tox runs (in parallel!).
Your Python applications are running but you’re wondering what they are doing? The only clue about their current state is the server load after ssh -ing into the servers? Let’s change that!
Setting up Python to the point to be able install packages from PyPI can be annoying and time-intensive. Even worse are OS-provided installations that start throwing cryptic error messages. Especially desktops are prone to that but it’s possible to break the whole toolchain of a server by installing some shiny package you heard about on reddit .
TLS is the best technology we have for securing our communications. It comes with many sharp edges though. This talk tries to jumpstart a rough understanding and these links should help you to complete the picture.
Apple ships a patched version of OpenSSL with macOS. If no precautions are taken, their changes rob you of the power to choose your trusted certificate authorities (CAs) and break the semantics of a callback that can be used for custom checks and verifications in client software.
A completely incomplete guide to packaging a Python module and sharing it with the world on PyPI.
Google killed its Reader and my beloved Reeder for Mac and iPad officially won’t get updated in time. I think to have found an adequate setup to replace both.