.NET Profilers are a developer’s best friend when it comes to optimizing application performance. They are especially critical when doing low level CPU and memory optimizations. But did you know that there are three different types of profilers? All are very valuable but serve relatively different purposes and different types of performance profiling. Let’s explore the different types.
Digitalization is sweeping across many industries, creating a huge need for innovation. This innovation forces companies to be more agile and deliver faster. However, increasing the speed of your development team doesn’t happen by magic. It’s a metric that isn’t easy to change. How can you adjust to this fast-paced digitalization? I want to introduce you to DevOps.
Have you heard the saying “you can’t manage what you can’t measure?” The phrase is often attributed to management thinker Peter Drucker, and less often to other authors as well. It can also slightly vary in its wording, depending on the source. At the end of the day, the exact wording of the sayer and who said it first doesn’t matter as much as whether it’s true or not. We’re here to tell you that yes, it’s true for a lot of things in life.
Over the last decade, DevOps has become an important part of software engineering culture, influenced by the wide adoption of microservices, containers and cloud computing. A recent step in the evolution of cloud-based and microservice architecture is the serverless computing – a code execution model where the cloud provider takes total responsibility for the operating system and hardware management.
Debugging performance issues in production can be a pain and, in some cases, impossible without the right tools. Java profilers have been around forever, but the profilers most developers think about are only one type: standard JVM profilers. However, using one type of profiler is not enough. Suppose you’re analyzing your application’s performance. There are multiple profiling activities which you may execute.
Imagine you’re driving a car, but there’s no windshield. You can’t see in front of you. On top of that, your car is full of friends looking out the side windows and yelling various things to you: “We should turn left!” “No, we should turn right!” “I’m pretty sure the next turn is in two miles.” In this scenario, how likely are you to reach your destination instead of ending up in a ditch or careening off a bridge?
If you’ve been writing code for any reasonable amount of time, then it’s virtually impossible that you haven’t handled logging in any way, since it’s one of the most essential parts of modern, “real life” app development. If you’re a .NET developer, then you’ve probably used some of the many famous logging frameworks available for use at this platform. Today’s post will cover one of these frameworks: log4net.
The software development life cycle (SDLC) started to become a formalized process for software development in the 1960s. That’s pretty much when software systems started to exist. With its loosely defined development phases, the SDLC has become a software development term that gets a lot of people talking. The SDLC, however, is more of an acronym packed with meaning than a specification or framework for software development.
Managing an application’s performance, and specifically keeping it in good condition, is one of the hardest challenges in software development. That’s true for virtually any programming language and platform. Java is no exception to this rule. But beyond that, Java also presents some unique challenges of its own.
