Today is September 19th, 2016. I can’t help but think about how much the tech world has changed in just a few short years. Kubernetes is really starting to take off, and alongside it comes Helm, Istio, Envoy—so many new tools that promise to make our lives easier while adding complexity at every turn.

At work, we’re pushing hard on scaling out some of our applications using Kubernetes. The idea of containers and orchestration was tantalizing when we first started dabbling with Docker a couple years back, but the reality is much more nuanced. Kubernetes has been a godsend for managing stateless services, but we’ve run into some issues scaling up to handle peak loads.

One particular day stands out in my mind. It’s a Tuesday morning and the logs are screaming: “Out of pods!” The application, which normally handles 100 requests per second during off-peak hours, is suddenly handling over 10x that. Our monitoring tool, Prometheus, was tracking everything from CPU usage to network latency, but something was still missing.

We had a debate in the team about whether we should just scale out more nodes or fine-tune our resource limits and requests. The argument for scaling was simple: it’s easier to add more hardware than to tweak complex resource constraints. But that felt like a Band-Aid solution, masking the real issue of how our application handles load.

I spent most of that day debugging, trying different configurations with Kubernetes’ kubectl commands. I wanted to optimize resource allocation so we could handle spikes without having to scale out all the time. We tried adjusting limits and requests on individual pods, but it was a frustrating process. Sometimes I’d think we were getting somewhere, only to find the application was still hitting hard limits.

By late afternoon, the application had stabilized again, and the logs stopped complaining about being out of pods. But as soon as the load dropped back down, I knew this was just a temporary fix. We needed a more robust solution that could handle real-world spikes without breaking.

Later that evening, I sat down to reflect on what we had accomplished that day. The application was still running, and our users were happy, but it felt like we were constantly firefighting rather than building something sustainable.

I also spent some time thinking about the wider tech landscape. Kubernetes’ rise is just one piece of a much larger puzzle: serverless architectures with AWS Lambda, Terraform 0.x rolling out its new features, and GitOps starting to gain traction. It’s an exciting time, but there’s no denying that it can be overwhelming.

As I look back at the day, I realize that while we might not have fully solved our scaling issues yet, we made progress. We’re one step closer to a more resilient system. And that’s what matters most—continuously improving and learning from each challenge we face.

So here’s to another day in tech, filled with complexity and opportunities for growth. I’ll be back tomorrow with whatever new challenges come our way.

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This post is written as an honest reflection on the struggles of scaling applications using Kubernetes during a time when many new tools were emerging. It captures both the technical details and the broader industry context.