If you’re reading this article, chances are you’re about to start moving into Azure and want to get your cost control right from the start, or you’re already on your journey and are running into problems getting a clear view of where you’re spending your money. Moving into Azure, or any other cloud, brings with it a new set of challenges that you just don’t face on-prem, and the big one is that pretty much everything incurs its own running cost.
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.
Today we’re pleased to announce the general availability of InfluxDB Enterprise on Microsoft’s Azure Marketplace. We’ll dive into all of these below, but first, let’s take a step back in case you’re not familiar with time series databases. If you’re looking for a time series database, here are three things to look for.
Jaeger is a popular distributed tracing project hosted by the Cloud Native Computing Foundation (CNCF). In the Elastic APM 7.6.0 release we added support for ingesting Jaeger traces directly into the Elastic Stack. Elasticsearch has long been a primary storage backend for Jaeger. Due to its fast search capabilities and horizontal scalability, Elasticsearch makes an excellent choice for storing and searching trace data, along with other observability data such as logs, metrics, and uptime data.
A Content Delivery Network (CDN) is a collection of distributed nodes, called edge servers, connected to the same origin servers and placed local to the users’ location. If you are using a CDN, your website content is delivered to the user from the nearest edge server to their location. Without a CDN, you are putting stress on the origin server every time a user requests something.
