Python’s magic methods - also known as dunder (double underscore) methods - can be used to implement a lot of cool things. Most of the time we use them for simple stuff, such as constructors (__init__), string representation (__str__, __repr__) or arithmetic operators (__add__/__mul__). There are however many more magic methods which you probably haven’t heard about and in this article we will explore all of them (even the hidden and undocumented).

Kubernetes v1.25 introduced Container Checkpointing API as an alpha feature. This provides a way to backup-and-restore containers running in Pods, without ever stopping them.

This feature is primarily aimed at forensic analysis, but general backup-and-restore is something any Kubernetes user can take advantage of.

So, let’s take a look at this brand-new feature and see how we can enable it in our clusters and leverage it for backup-and-restore or forensic analysis.

Out-of-the-box, the Python standard library ships with many great libraries, some of which provide CLIs, allowing us to do many cool things directly from terminal without needing to even open a .py file.

This includes things like starting a webserver, opening a browser, parsing JSON files, benchmarking programs and many more, all of which we will explore in this article.

The assignment operator - or walrus operator as we all know it - is a feature that’s been in Python for a while now (since 3.8), yet it’s still somewhat controversial and many people have unfounded hate for it.

In this article I will try to convince you that the walrus operator really is a good addition to the language and that if you use it properly, then it can help you make your code more concise and readable.

With every Python release, there are new modules being added and new and better ways of doing things get introduced. We all get used to using the good old Python libraries, but it’s time say goodbye to os.path, random, pytz, namedtuple and many more obsolete Python libraries and start using the latest and greatest ones instead.

As files, datasets and configurations grow, it gets increasingly difficult to navigate them. There are however many tools out there, that can help you to be more productive when dealing with large JSON and YAML files, complicated regular expressions, confusing SQL database relationships, complex development environments and many others.

In this article we take look at how you can use Python’s Kubernetes Client library to automate all the boring Kubernetes tasks and operations, such as creating/patching resources, watching events, accessing containers and more.

With the recent events relating to Google Analytics platform, it’s becoming very clear that the time has come for many of us to migrate from Google Analytics to different platforms.

In this article we will go over both the “Why?”, so that you can make an informed decision whether you need to migrate of not, as well as the “How?” of migrating from Google Analytics - that is, quickly and easily taking your data and moving to different analytics platform without too much hassle.

Before any new feature, change or improvement makes it into Python, there needs to be a Python Enhancement Proposal, also knows as PEP, outlining the proposed change. These PEPs are a great way of getting the freshest info about what might be included in the upcoming Python releases. So, in this article we will go over all the proposals that are going to bring some exciting new Python features in a near future!

If you’re using GitHub as your version control system of choice then GitHub Apps can be incredibly useful for many tasks including building CI/CD, managing repositories, querying statistical data and much more. In this article we will walk through the process of building such an app in Go including setting up the GitHub integration, authenticating with GitHub, listening to webhooks, querying GitHub API and more.

Martin Heinz on the tools/techniques for finding bottlenecks in your Python code. And fixing them, fast.

The first rule of optimization is to not do it. If you really have to though, then optimize where appropriate. Use the above profiling tools to find bottlenecks, so you don’t waste time optimizing some inconsequential piece of code. It’s also useful to create a reproducible benchmark for the piece of code you’re trying to optimize, so that you can measure the actual improvement.

In the recent months there’s been a lot of noise in the area of supply chain security because of increase in attacks, with notable ones like Microsoft Exchange Server or SolarWinds breach. These attacks could have been prevented with proper tools in place, yet finding the right tool for the job might be difficult as this area is hard to navigate and most of us - developers - aren’t security experts. There’s however a project that can solve this. Its name is sigstore and in this article we will look at what it does, why we need it and how it fits into landscape of existing tools in this area.

At the first glance Python’s operator module might not seem very interesting. It includes many operator functions for arithmetic and binary operations and a couple of convenience and helper functions. They might not seem so useful, but with help of just a few of these functions you can make your code faster, more concise, more readable and more functional. So, in this article we will explore this great Python module and make the most out of the every function included in it.

In any software project, over time new features and APIs are added and from time-to-time some of them also become deprecated and eventually get removed. Even huge project such as Kubernetes is no exception to this, yet core parts of its API don’t really come to mind when thinking about deprecating and eventual removal. So, the question is - could a core object or API in Kubernetes, such as Pod, Deployment or Service be removed and if so, how would that go?

Take a tour of Python’s functools module and learn how to use its higher-order functions to implement caching, overloading, creating decorators and in general how to make your code a bit more functional.

Debugging containerized workloads is a daily task for everyone who works with Kubernetes, which can be made much simpler with kubectl debug - a beta feature of Kubernetes. In the article you will learn how to make it available in your cluster, how it works, as well as some examples how you can use it to easily debug both Kubernetes Pods and worker Nodes.

Working with Docker CLI is very straightforward - you just build, run, inspect, pull and push containers and images, but have you ever wondered how do the internals behind this Docker interface actually work?

Behind this simple interface hides a lot of cool technologies and in this article you can learn about one of them - the union filesystem - the underlying filesystem behind all the container and image layers.

Martin Heinz shares some of his favorite git features: word diff, auto-correct, plugins, and commit signing.

At this point probably everybody has heard about Docker and most developers are familiar with it, use it, and therefore know the basics such as how to build a Docker image. It is as easy as running docker built -t name:tag ., yet there is much more to it, especially when it comes to optimizing both the build process and the final image that is created.

The article goes on to cover caching, slimming, and securing your images so they’ll run faster and be less prone to abuse.

Container security is often overlooked topic, as people assume that containers are secure by default - which is not true. One of the ways to secure container workloads in Docker and Kubernetes is to leverage seccomp profiles and this advanced feature of container runtimes is explained and shown in this article.