Debugging Kubernetes with Kubernetes

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January 2015 was a month of many firsts for me. I had just started working on our new containerized microservices platform based on Docker and Kubernetes, and the learning curve was steep. Kubernetes had been announced by Google in November 2014, but it was still very much in its infancy compared to what we would see later.

The Setup

We were using CoreOS as our base image for Docker containers and etcd for managing cluster state. We also integrated with Marathon, which is another container orchestration tool from Mesosphere. But as often happens, the devil is in the details, and our setup started to show some cracks under load.

Debugging the Cracks

One of the biggest issues we encountered was around resource management. Our services were running on Kubernetes nodes, but we found that memory pressure was causing unexpected outages. We had a service running that handled real-time processing for our customer data, and it was failing at seemingly random intervals—only to come back up once the load decreased.

Step 1: Logging and Metrics

First, we turned to our logging infrastructure (Fluentd + Elasticsearch) to get a better picture of what was happening. The logs showed spikes in CPU usage leading up to service crashes, but they didn’t provide enough context about memory or other resource bottlenecks.

Step 2: Kubernetes Events

Next, I dug into the Kubernetes events. Kubernetes keeps track of all the actions it performs and errors that occur. We found a pattern where nodes were being evicted due to insufficient resources, which would cause our services to crash. This was happening because we hadn’t properly configured resource limits and requests for our containers.

Step 3: Resource Requests and Limits

After fixing some of the container configurations, I wrote a small script to update the YAML manifests in bulk. The script went through each service, adjusted their resource requests and limits, and redeployed them one by one. It was tedious but necessary.

Lessons Learned

Debugging Kubernetes wasn’t just about finding the right tool or configuration; it was about understanding the system from the ground up. I spent hours poring over documentation, trying out different configurations, and watching the nodes closely for signs of failure.

One thing that became clear is that Kubernetes is a powerful tool, but it requires careful tuning to work well. We had to be mindful of how our services interacted with each other and with the underlying infrastructure.

The Future

By February 2015, I had shipped a more robust version of our containerized platform. It included better resource management, enhanced logging, and improved monitoring. While we still faced some challenges, the groundwork we laid in January set us up for success as Kubernetes matured over the following months.

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January 26, 2015, was just one day in a long journey of learning how to work with Kubernetes and containers. But it was a crucial step in understanding that debugging is an ongoing process, and that each failure brings you closer to a more stable and reliable system.