Recently, I had a situation where I needed to introspect on the memory of a java program running on kubernetes. The usual procedure is connecting to the container, getting a dump, collecting this dump from a standalone laptop/host and analyzing it offline. However, I wanted to explore some other alternatives, so I would get here some additional techniques that can be used either from developers or for operations doing debugging or forensics.

Prometheus and grafana have been established as de-facto stacks for kubernetes cluster monitoring, at least in the upstream communities. Once a cluster has the stack available, kubernetes and other application components are exposing their metrics. There is a need to create curated alerts based on those metrics and dashboards that give a high level view of what is happening. There is an effort to create those artifacts in a templated way.

One of the most appealing features of container orchestrators like kubernetes is the ability to adapt the resources consumption to the need at that moment. At some point, the architects start to think about scaling the workloads in a smart way. Kubernetes provides two native mechanisms for autoscaling the workloads, the horizontal pod autoscaler (hpa) and the vertical pod autoscaler (vpa), however the latter in my opinion is not a transparent scaler.