A Day in the Life of September 11, 2017

On this day back in 2017, I was knee-deep in a whirlwind of platform engineering challenges. Kubernetes was starting to solidify its position as the de facto container orchestration tool, and with it came a host of new tools like Helm, Istio, Envoy, and Terraform. The term “GitOps” had just started to gain traction, and everyone seemed to be on a quest for better observability—Prometheus and Grafana were replacing the older Nagios in many circles.

A Debugging Odyssey

It was early morning when my pager went off. Another one of our microservices had gone down, this time with some strange errors that had been elusive so far. I grabbed my laptop and headed to the server room, feeling like a medieval troubleshooter setting out on a quest.

The service in question used Kubernetes for orchestration and Istio for service mesh. The error logs showed a mix of network timeouts and unexpected responses. After a few fruitless hours of digging through the usual suspects—pod logs, containerd logs, and Istio’s tracing—I decided to take it up a notch.

I dove into the Prometheus data, pulling up graphs that showed fluctuations in request latency over time. Something wasn’t right; these spikes didn’t match any known patterns. I spent another hour cross-referencing this with Grafana dashboards, trying to find some hidden correlation. Finally, the breakthrough came: a spike in network traffic correlated with the errors.

The Serverless Lament

As I delved deeper, my thoughts turned to serverless and AWS Lambda, which was all the rage at the time. The idea of not having to manage servers was appealing, but it wasn’t without its pitfalls. I had heard about companies moving their architectures towards serverless and then realizing they needed more control over their infrastructure.

One late night, I found myself arguing with a team that wanted to fully embrace serverless for everything. “But what if you need to debug an issue?” I asked. “Where do you look? How can you instrument your code effectively?”

They had their points too—serverless promised elasticity and reduced management overhead. But the trade-offs were real, especially when things went wrong.

The GitOps Revelation

On a lighter note, the term “GitOps” was gaining traction, and I found myself reading more about it. The idea of using Git as the single source of truth for infrastructure configuration was intriguing. We had experimented with Terraform in our platform, but there were still plenty of manual steps that needed to be automated.

One weekend, I sat down to set up a GitOps pipeline for one of our microservices. It involved writing a script to clone the service’s repository, run Terraform, and push any changes back to the main repo. The whole thing felt clunky, but it was a step in the right direction.

Personal Reflections

Reflecting on that day, I realized how much had changed since 2015. Kubernetes was no longer just an experiment; it was becoming a fundamental part of our infrastructure. Helm and Istio were making container management more accessible, while serverless seemed like the future, though with its own challenges.

Prometheus and Grafana had become essential tools for monitoring, replacing the older Nagios systems that we had used before. GitOps was gaining momentum, promising better collaboration and automation.

But even as new technologies emerged, the core issues—debugging, scaling, and managing complexity—remained constant. We were still grappling with the same problems, just in different guises.

The Day’s End

As I shut down my laptop for the night, I couldn’t help but think about where we would be in five years. Would serverless have won out, or had Kubernetes carved a permanent place in our toolbelt? Only time would tell.

For now, I was just glad to have made it through another day of platform engineering. It wasn’t always easy, but there were moments of real satisfaction when the service worked as intended and we could celebrate a successful deployment.

September 11, 2017—another day in the life of a platform engineer, full of challenges and discoveries.

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That’s how it felt back then. The tech landscape was constantly shifting, but the core work remained challenging yet rewarding.