July 10th, 2017. I remember that day well—Kubernetes had already won the container wars, but we were still grappling with its complexities and nuances. At work, our team was starting to dip our toes into Kubernetes, and boy, did it give us a run for our money.

Our previous setup revolved around Docker Swarm, which was relatively straightforward (or so we thought). But as soon as we started moving some of our applications over to Kubernetes, I found myself in a peculiar situation. Our application logs were telling a story—albeit not one we wanted to hear. Specifically, the pod crashes and restarts were becoming more frequent than I liked.

I spent hours poring over log files, trying to figure out why certain pods were failing so often. After days of debugging, I realized that the issue was somewhat meta: it wasn’t just the application code causing problems; Kubernetes itself was throwing some curveballs.

Here’s a snippet from one of our logs:

2017-07-10 15:46:30.989 [INFO] pod "app-74d9bf6b68-7c58l" is restarting due to unhandled crashloopbackoff

This log entry was like a wake-up call. We had been so focused on getting our applications into containers and then into Kubernetes that we hadn’t really stopped to consider the health checks and liveness probes properly.

The Liveness Probes Dilemma

One of the biggest issues came down to our liveness probe configurations. Initially, they were set too aggressively—basically probing every single second. This led to constant restarts because even minor network latency would cause a failure. It was like trying to catch a fish by fishing with dynamite.

I had to adjust these probes to be more realistic and less aggressive. We started tuning them based on actual application behavior, not just generic settings. For example:

livenessProbe:
  httpGet:
    path: /healthz
    port: 8080
  initialDelaySeconds: 15
  periodSeconds: 30

This change made a world of difference—reducing unnecessary restarts and giving our applications more breathing room.

Embracing the Chaos

Another challenge was learning to embrace chaos. Kubernetes introduced this concept through its Operator model, which allowed us to write custom controllers that could handle complex state management for our applications. At first, it felt like stepping into a maelstrom of complexity. But after some trial and error, I realized the power it held.

For instance, we built an operator that managed the reconciliation loop for our database pods. It automatically scaled them up or down based on metrics collected from Prometheus, ensuring optimal performance while minimizing costs.

apiVersion: operators.coreos.com/v1alpha1
kind: Operator
metadata:
  name: db-operator
spec:
  clusterServiceResources:
    resources:
      - apiGroup: example.com
        kind: Database

The Zeitgeist

Looking back, July 2017 was a fascinating time in tech. We were amidst the hype of Kubernetes and serverless architectures like AWS Lambda. At the same time, server-side JavaScript (Node.js) was becoming more popular, and platforms like Firebase were making full-stack development easier than ever.

But for me, it was all about finding that balance between robust infrastructure and agile application deployment. The tools we used—like Helm to manage our Kubernetes deployments, or Grafana + Prometheus for monitoring—were evolving rapidly. Each new update brought both excitement and frustration as we tried to keep up with the changes.

Reflection

In the end, it was a good reminder that no matter how advanced your technology stack is, the real work often comes down to understanding the behavior of your applications in context. Kubernetes, for all its power, can still trip you up if you don’t pay attention to the details. But with a bit of patience and fine-tuning, we managed to get our house in order.

That’s my July 10th, 2017, tech journal entry. Hope it resonates with anyone else who has faced similar challenges on their journey with Kubernetes.