Debug logs are incredibly valuable for the wealth of information they contain, but generally speaking, companies avoid collecting them in production because they are very expensive in both dollars and performance. When we talk about production observability, we are likely spending more money for more data, data that itself might be excessive.

Kubernetes is both revolutionary and “diffusionary.” It is a complete restructuring demanding a whole new slew of companion and support tools to cover and prop up the entire ecosystem. There are literally hundreds of tools – both open-source and proprietary – designed specifically with k8s in mind.

Developers are talking about Google’s latest creation: Carbon, a supposed wunderkind programming language that will save the technoverse from C++ and serve as its successor or replacement. Just like a rehashed Hollywood blockbuster about a supposed messiah, we’ve heard this story before. The 2000s saw more than one language try to fix C++’s minuses. The two big ones were Rust (backed by Mozilla) and Go (a.k.a. Golang, initiated by Google).

Rust and Go in some ways are polar opposites. They are defined by their respective approaches to code. Rust is scrupulous, meticulous, and demands painful attention to detail; Go is laidback by contrast, easygoing, and simple. They both prize efficiency, but one in the means (Go) and the other in the ends (Rust). A true Go vs. Rust battle needs to compare the two languages in more depth. Golang and Rust owe their births to loathing for other systems languages (and by languages, I mainly mean C++).

There can be confusion out there given the size of the JS ecosystem. The differences seem pretty trivial, as a lot of tools seem to work in both limited and expanded capacities. But knowing the original or primary intent of a tool like React.js (it’s a library), can make it easier to grasp how it works and with which other JS tools. But this is where the distinctions become important – understanding the role of a progressive framework like Nest.js and how we got here.

Python really needs no introduction, but Google’s search algorithms demand it. Python is essential for its versatility and low barrier of entry for new users. It’s become a vital part of nearly every corner of development, especially in big data and machine learning which may not have progressed to where they are in 2022 without Python’s inception. All the same, debugging Python needs to be efficient.

The only thing better than one awesome (and extremely useful!) tech tool is an integration with another similarly great tool. That’s why we’ve been working on building up our collection of new integrations because our main goal, always, is to make developers’ lives easier. And we do that by allowing you to immediately get all the live data you need from Rookout straight to your favorite tool. And the next step in doing so?

Spring Boot has made the Spring Framework more accessible than it already was. It is a streamlined form of the larger Spring Framework. For one, Spring uses manual configurations while Spring Boot contains a number of default config templates. Spring has a number of dependencies; Spring Boot, not so much (at least until build time). Auto-configuration makes it easy to get started with the Spring Framework (or even Java overall if you’re a true novice to coding) and the support community is huge.

Developers update and augment their stacks all the time. No configuration is permanent, and a new tool can enter the picture at any given moment. The reasons vary, but new applications’ addition to the mix can upset your integrations from time to time, particularly if you’re adding security tools. Rookout is always aiming to support a fluid experience, and that’s what is behind our latest updates to our Gitlab integration.

Most programming languages are built on procedural programming, but there is this niche of dialects built for “functional programming,” where code classes are dynamically created based on the rules in the functions you write. If you could put languages on a scale of most procedural to most functional, you’d find Clojure sitting at the edge of the latter. What is Clojure? Who uses Clojure? What’s the best Clojure use case?