Operations | Monitoring | ITSM | DevOps | Cloud

DevOps monitoring didn’t simply become part of the collective engineering consciousness. It was built, brick by brick, by practices that have continued to grow and flourish with each new technological innovation. Have you ever been forced to sit back in your chair, your phone buzzing incessantly, SSH windows and half-written commands dashing across your screen, and admit that you’re completely stumped? Nothing is behaving as it should and your investigations have been utterly fruitless.

Open-source software started around the millennium and is now one of the cornerstones of modern software development. Open-source projects make their source code available to anyone so that engineers across the world can inspect the code to find bugs or make changes to suit their needs. Today, there are more than 180,000 open-source projects available, according to Wikipedia. We at StackState are big believers in open-source software.

Choosing between blade servers or those intended to go installed in a rack is a small headache that is repeated daily in the complex minds of technicians around the world. What configuration to choose? What can best serve my installation? These are questions that are repeated while they pull out their silvery and silky hairs.

Application performance monitoring (APM) and logging both provide critical insight into your ecosystem. When paired together with context, they can provide vital clues on how to resolve problems with your applications. As the log data you analyze becomes more complex, navigating to the relevant pieces can be tricky using traditional tools. With Elastic Observability (powered by the Elastic Stack), correlating logs with APM is as simple as a few clicks in Kibana.

At Stackify, we’re all about helping you improve your application’s performance. We have actually developed two code profilers ourselves. Because of that, we like to think we know a thing or two about code profiling. Today I want to talk about the three different types of code profilers, describe the differences between them, and recommend some tools for your toolbox.