After playing around with a few Docker containers and Docker
compose, I decided it was time to dive into Kubernetes.
But I only have one server:
lexicon.
The young artist in the house, who will soon have an upgraded
PC for the new year, will be then running
Ubuntu Studio22.04,
made for creative people.
The current system is running Ubuntu Studio 20.04 on a very
old system, based on an AMD Phenom II X4 and an Asus M4A89GTD
Pro/USB3 from 2010. In preparation for the upcoming upgrade,
the process starts by installing Ubuntu Studio 22.04 on a old
SSD on my own PC (Rapture), which can later be
transplanted to the new PC.
But only a little bit, maybe just enough to run a Minecraft
server, which refuses to start on my Raspberry Pi 4
because it has only a meagre 2 GB of RAM.
I had known about Intel NUC tiny PCs for a while, and
how handy they can be to have a dedicated physical PC for
experimentation. There was a very real possibility
that I would have to set one up as a light gaming PC in the
near future, so I thought cutting my teeth on a simpler
server setup would be a good way to get acquainted with this
hardware platform and its Linux support.
In fact, when I started building detailed process monitoring
I knew nothing about
telegraf,
influxdb,
grafana
or even Raspberry Pi computers.
It was back in 2017, when pondering whether to build my next
PC around an
Intel Core i7-6950X
or an
AMD Ryzen 5 1600X, that I started looking into measuring
CPU usage of a specific process. I wanted to better see and
understand whether more (but slower) CPU cores would be a
better investment than faster (but fewer) CPU cores.
At the time my PC had a
AMD Phenom II X4 965 BE C3
with 4 cores at 3.4GHz, and I had no idea how often those CPU
cores were all used to their full extent. To learn more about
the possibilities (and limitations) of fully
multi-threading CPU-bound applications, I started running
top commands in a loop and dumping lines in .csv files to
then plot charts in Google Sheets. This was very crude, but
it did show the difference between rendering a video in
Blender (not multi-threaded) compared to using the
pulverize tool to
fully multi-thread the same task:
This early ad-hoc effort resulted in a few scripts to
measure per-proccess CPU usage, overall CPU with thermals,
and even GPU usage.
