1 Fundamental Principle for Kubernetes Cluster Optimization

Let’s say you have a small Kubernetes cluster. It’s small, simple and wonderful. With few nodes and pods, the cluster can struggle under high network load.

This could be because of resource-intensive workloads. But, scaling up isn’t always the solution.

Note: Sometimes tcpdump can be a handy to fix those issue. Do check it out, it’s short and a simple demo.

A larger cluster with more resources can provide more capacity, but brings new challenges my friend. More nodes is not always more stability. In fact, you can get new complexities.

Understand this: The real magic of K8s lies in its ability to distribute workloads.

More worker nodes give you more resources to play with, sometimes they hide the limitations of your individual components or applications (But it can’t hide everything)

So when you’re planning for optimization, do a load test. A load test that impacts everything to match your customer needs.

Look for things that break first and fix it. Then go to the next one. Repeat the process until you have enough resilience.

This applies to both your infrastructure and your applications — sometimes (or maybe so many times) you may have to tweak your app too.

Few things to remember:

1. Right size your cluster based on your actual needs (complex part, I know, but load test helps!)
2. Resource allocation is key — set appropriate requests. And limits if needed. Choose wisely if setting CPU limits.
3. Use monitoring tools (prometheus, grafana and jaeger) to spot and fix bottlenecks.
4. Optimize both your apps and your infrastructure.

If you are fine, always choose for multiple smaller cluster. Many people told me that this is the way to go. Although few people explored the other side. But that’s for another day’s discussion.

The goal is always to find the sweet spot. A sweet spot where your cluster is just the right size to handle your workloads efficiently, without getting you and your team into trouble. It’s all about balance my dear friend.