Today, IT and site reliability engineering (SRE) teams face pressure to remediate problems faster than ever, within environments that are larger than ever, while contending with architectures that are more complex than ever. In the face of these challenges, artificial intelligence has become a must-have feature for managing complex application performance or availability problems at scale.
For IT Operations and Site Reliability Engineering (SRE) teams, logging is nothing new. In fact, collecting and analyzing logs is one of the oldest cornerstones of performance management. Logs have been part and parcel of APM workflows for decades. Yet the logging strategies that worked in eras past often fall short today. That’s thanks to the advent of cloud-native computing, which has ushered in fundamental new challenges in the way teams aggregate, analyze, and manage logs.
In this blog post, we will walk you through how to develop a continuous performance management playbook for your business to effectively integrate and manage performance as an integral part of your development process – enabling you to build fast experiences faster than your competition. Let’s dive in!
For DevOps teams, delivering quality software has long required reconciling a major tension: In a perfect world, you’d catch every issue in each new release of your application before you deployed the release into production. But in the real world, doing so is tricky, not least because it’s hard to collect data about application performance before the application is actually deployed.
In the present age of cloud-native everything, it can be easy to forget that some applications still run on-premises. But they do and managing the performance of on-premises apps is just as important as monitoring those that run in the cloud. With that reality in mind, here’s a primer on how to approach on-premises application performance monitoring as part of a broader cloud-native performance optimization strategy.
One of the first things you’ll learn when you start managing application performance in Kubernetes is that doing so is, in a word, complicated. No matter how well you’ve mastered performance monitoring for conventional applications, it’s easy to find yourself lost inside a Kubernetes cluster.
The management of modern software environments hinges on the three so-called “pillars of observability”: logs, metrics and traces. Each of these data sources provides crucial visibility into applications and the infrastructure hosting them. For many IT operations and site reliability engineering (SRE) teams, two of these pillars — logs and metrics — are familiar enough.
Whether you’ve heard of or fully jumped on the DevOps or SRE bandwagon, you may have also wondered how the two relate. What’s the difference? Are they really just different ways of looking at the same problem? The term DevOps hit the market first, but SRE wasn’t too far behind. And though they have different origin stories, they both focus on autonomy, automation, and iteration. So why do these paradigms exist? And why do we need both? Let’s look at this further.
The latest version of the Splunk Operator builds upon the release we made last year with a whole host of new features and fixes. We like Kubernetes for Splunk since it allows us to automate away a lot of the Splunk Administrative toil needed to set up and run distributed environments. It also brings a resiliency and ease of scale to our heavy-lifting components like Search Heads and Indexer Clusters.
