Title: Debugging a Kubernetes Cluster in 2016

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January 4, 2016. It’s been almost two months since we decided to go all-in on Kubernetes at work, and I’m already knee-deep in it. The container wars have raged for some time now, but Kubernetes seems like the clear winner with its growing ecosystem and the momentum it has gathered. We’ve made the leap from Mesos to Kubernetes, and while the transition was smoother than we expected, we’re still facing a few challenges.

The other day, I found myself on a debugging marathon. Our staging cluster had gone down for no apparent reason, and we were pulling our hair out trying to figure it out. The first thing that jumped out at me was a flood of “out of memory” logs from the kubelets. This isn’t unheard of in Kubernetes clusters where resource management is still an art form rather than a science.

I started by checking if our nodes had enough resources allocated—CPU, RAM, and disk space. Our cluster was configured with default limits, but it seems that some pods were hitting these limits harder than we anticipated. I pulled up the kubectl top node command to get a quick snapshot of what was happening on each node. Sure enough, two of our nodes had 90%+ CPU usage, and one was even pushing 75% of its memory.

This brought me to my first thought: maybe it’s time to bump up the resource limits for our pods. But before I did that, I decided to take a deeper dive into the logs from those problematic containers. The logs themselves were cryptic—just errors and stack traces without much context. I spent hours scouring through them, trying to piece together what was happening.

After a lot of digging, I came across a curious pattern: the pods were failing because they couldn’t connect to our database. This seemed odd, given that the database had been running fine for months. Then it hit me—maybe this wasn’t just about resource constraints after all. Maybe something else was going on with the network.

I switched over to kubectl get events and found some interesting messages: “Network plugin couldn’t configure pod [POD_NAME]”. This pointed towards a networking issue, possibly related to our CNI (Container Network Interface) setup. After cross-referencing this with our recent changes in the cluster’s networking configuration, I realized that we had recently updated to Calico as our network provider.

I spent some time verifying the Calico configuration and ensuring everything was set up correctly. Once I confirmed that all the necessary configurations were in place, I redeployed the pods and watched them come back online one by one. Success! The staging cluster started humming again, and we were able to catch a few winks of sleep.

Debugging Kubernetes clusters is like solving a complex puzzle where every piece matters. The tools are powerful but can be overwhelming when you’re dealing with a production system that needs to stay up 24/7. This experience reinforced my belief in the importance of proper monitoring and logging practices, something I’ve been advocating for since day one.

As we continue to refine our Kubernetes setup, I’m looking forward to leveraging some new tools like Prometheus and Grafana, which are starting to gain traction in the community. These will help us keep a closer eye on the health of our cluster and give us insights into performance bottlenecks before they become critical issues.

In the meantime, I’ll be keeping my eyes peeled for any Kubernetes-related news or blog posts that might offer some useful tips or tricks. The tech landscape is moving fast, but so are we, trying to keep up with all the changes and challenges that come with it.

Stay tuned for more adventures in platform engineering!