Java developers are unlikely to ever suffer from a lack of libraries, utilities, and programs at their disposal. There's no shortage of tools that offer niche as well as fundamental features. However, some tools undoubtedly stand out due to their popularity, usefulness, data representation, and in-tool features. These 8 tools listed below are often listed as some of the best Java development tools available on the market.
You finish writing your code and launch your application. Then, you begin experiencing performance issues. How can you fix this? It doesn’t matter how talented your development team is, every code should always be analyzed, debugged, and reviewed to make it run faster. What you need is a performance profiling tool. In this article, you will learn about performance profiling and how to determine the best performance profiling tools for your software.
It’s never too late to learn. As any reinforcement learning agent, we get rewarded by the new knowledge that we acquire. Likewise, we learn by doing, by rolling up our sleeves and getting to work. (Do you want a hands-on book on Reinforcement Learning? Here is my personal favourite) March has shown us great examples of this. From robots learning to encourage social participation to detect serious environmental problems, it was a learning month.
Yes, there is the ability to silence or disable alerts in Graylog. There are times in IT environments where you know you are going to generate specific events in your network. As an example, you are patching servers, upgrading hardware components, and many other things. These types of activities are very common during maintenance windows.
Stackery can be used to create a new CloudFormation template or to quickly visualize an existing one. Code is automatically generated as you simply drag-and-drop resources on a graphical grid. The experience is much more intuitive than previous generation tools like AWS CloudFormation Designer. Stackery visualizes resources the way a human would perceive them, grouping related resources together.
During the next six weeks, our team will work to improve the overall experience of Qovery. We gathered all your feedback (thank you to our wonderful community 🙏), and we decided to make significant changes to make Qovery a better place to deploy and manage your apps. This series will reveal all the changes and features you will get in the next major release of Qovery. Let's go!
Logz.io has pressed hard to align our tracing and metrics analytics capabilities over the past year. And as our technology advances, so does our service. We are announcing Multiple Tracing Accounts with Logz.io Distributed Tracing, aligning it with our logging and metrics tools. Complementing multiple data sources for metrics and logs, Logz users can segment their data according to sources and teams for better organization.
For the first time, a software load balancer exceeds 2-million RPS on a single Arm instance. A few weeks ago, while I was working on an HAProxy issue related to thread locking contention, I found myself running some tests on a server with an 8-core, 16-thread Intel Xeon W2145 processor that we have in our lab. Although my intention wasn’t to benchmark the proxy, I observed HAProxy reach 1.03 million HTTP requests per second.
One of the big questions in monitoring can be summed up as: Who watches the watchers? If you rely on Prometheus for your monitoring, and your monitoring fails, how will you know? The answer is a concept known as metamonitoring. At Grafana Labs, a handful of geographically distributed metamonitoring Prometheus servers monitor all other Prometheus servers and each other cross-cluster, while their alerting chain is secured by a dead-man’s-switch-like mechanism.
