The vast majority of enterprises (77%) were expecting to move the bulk of their big data workloads to Kubernetes by the end of last year. Were you one of them? If so, you’re likely encountering an array of new challenges, including the struggle of properly allocating resources. That’s where Kubernetes monitoring can help. Keep reading to understand what Kubernetes monitoring is, why it matters, and snag five best practices for monitoring containers today.
Kubernetes monitoring is tracking the health and performance of Kubernetes workloads and microservice environments. By tracking key metrics like CPU, memory, and resource utilization, one can ensure their Kubernetes workloads are working optimally.
Kubernetes monitoring is crucial to ensuring the optimal performance of your containerized environments. Having the proper insight into cluster health, paired with alerts and recommendations, can drastically cut troubleshooting time, ensure workloads meet their promised SLAs, and prevent resource wastage from blowing your budget.
For tips on creating a Kubernetes strategy based on first-hand research, watch our webinar
Kubernetes Survey Results: What is Your Strategy?
It’s important to look at both your application and infrastructure metrics together. When working with Kubernetes, it’s common for developers to have an APM tool, and ITOps to have a Prometheus-backed infrastructure metrics tool. It’s also not uncommon for DevOps and ITOps teams to be siloed from one another. Unless you have both tools and teams working together, it becomes difficult to look into resource waste or determine which apps are good or bad citizens
Your cloud bill has a breakdown of costs, but there’s a lot that isn’t included in that breakdown. When does autoscaling happen? How big did your applications autoscale? Are you scheduling resources you aren’t using? This lack of visibility isn’t uncommon and has real consequences. In 2020, ⅓ of respondents surveyed expected to go over budget by between 20-40%. Trust your cloud bill, but understand what is behind the bill, too. This is made easier when you understand your applications and platform together.
In the Kubernetes space, you’re no longer dealing with just a set of 10-20 computers running one application. Now, we’re dealing with thousands of applications in a shared environment. You simply can’t manually tune every application and workflow with the precision and speed necessary to keep up with cloud scale. Automatic optimization is key to succeeding with Kubernetes. Manual tuning = wasted hardware resources, time, and effort.
Rather than combining multiple tools to provide you with the big picture you need for success with containers, we recommend using one tool to do it all. The Pepperdata solution is one tool that provides Kubernetes monitoring with best practices in mind. We provide the metrics of both the platform and applications in a single pane of glass dashboard.
With powerful automation, the Pepperdata solution allows you to run more apps on a smaller footprint or run applications quicker on less footprint to reduce costs. Our solution provides alerts and recommendations, so you can stay proactive and cut troubleshooting times. We also provide insight into resource usage, memory allocation, CPU, and more so you can fully understand what’s behind your cloud bill.
To quickly recap, we’ve covered what Kubernetes monitoring is, why it’s important, and five best practices we recommend following for success. Understanding your platform and applications together is key to meaningful insights, trust yet verify what’s going on behind your cloud bill, use automation to get ahead, alert on problematic events to stay proactive, and measure important metrics whenever possible.
If you’d like to learn more about Kubernetes monitoring, creating a Kubernetes strategy to tackle the new landscape, and more, watch our panel webinar: Kubernetes Survey Results: What is Your Strategy?