As developers, we spend a lot of time in our IDEs writing new code, refactoring code, adding tests, fixing bugs and more. And in recent years, IDEs have become powerful tools, helping us developers with anything from interacting with HTTP requests to generally boosting our productivity. So you have to ask — what if we could also prevent security issues in our code before we ship it?
A fundamental element of the Kubernetes microservices system is the services model, which gives teams greater understanding of how their applications are deployed. These objects running within pods and containers, by extension, are RESTful since they’re based on APIs. However, DevOps teams can’t hope to run a tight ship without managing their services. Communication and visibility are absolutely crucial in a Kubernetes system.
An application running in production is a difficult beast to tame. Most experienced developers–ones who spent enough late nights or Saturday mornings trying to break apart a nasty production bug–will try and create the clearest possible picture for their later selves while writing their code, so that they could understand what’s actually going on in the system during an incident.
You’re likely familiar with local debugging—the ability to go through your code line by line to find and eliminate bugs. However, with the ever-increasing complexity of development environments, working efficiently with remote systems is becoming more necessary. In this case, “remote” can mean any machine you don’t have native OS-level access to, such as Virtual Machines, Docker containers, and entirely separate devices accessed over the network.
After building Lightrun for the JVM – an easier way to get a better grasp on production applications written in Java, Scala, and Kotlin – we’re pleased to announce the release of Lightrun’s developer-native observability platform for Python!
As software got more complex, more and more software projects rely on API integrations to run. Some of the most common API use cases involve pulling in external data that’s crucial to the function of your application. This includes weather data, financial data, or even syncing with another service your customer wants to share data with. However, the risk with API development lies in the interaction with code you didn’t write—and usually cannot see—that needs debugging.
As a developer, you’re going to be making changes to a codebase. That’s why, as Harold Abelson put it, “Programs must be written for people to read.” If a codebase is not clearly formatted, debugging becomes more difficult than it should be. Though usually overlooked, little changes like reformatting and proper indentation of your code can obviously differentiate a professional developer’s code base from someone just learning.
Java is as old as the Internet itself. It is the child of the mid-90s and exploded into the world just as dial-up started to take over suburban phone lines. In a way, it can be seen as the original Boomer of programming languages — not as elderly ancient as the C-families, but not as young and hip as JavaScript and its multitude of frameworks and libraries either.
Though it has been around for some years now, Docker is still one of the most flexible, efficient, and nimble technologies for rapidly building environments to host software of all kinds—especially web-based applications.
