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.
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.
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.
We’ve already outlined why API performance matters and what aspects of APIs to test, but what is the difference between API testing and monitoring? As with most things, context matters. The use cases for testing and monitoring are different because the objectives are different. The ultimate goal is to verify that your APIs are functioning properly, but staging environments vary significantly from production environments.
Simple questions can be overwhelming and not knowing the answer after a mouse click is no longer an option: Sustainability is top of mind for organizations across all verticals and Splunk can help with the power of data. Our upcoming Sustainability Toolkit based on the Splunk platform equips organizations with capabilities to gain deep insights into their carbon footprint and as such empowers them to take the necessary actions towards their carbon neutrality goals.
