Statistics
phaidenbauer • 3 Jan 2026 •
Although I never took a statistics class, and to be honest, I’m not even the best when it comes to more advanced mathematics, I still like to keep everything that comes to mind in spreadsheets. Well, in my case, in PostgreSQL database tables (developing still feels like a superpower from time to time).
So, as I did some laundry today and recently started the oil usage of the house, I set out to set up another table for wood usage. Especially for the wood-burning oven in the basement, excluding the small Swedish oven in the living room.
I know PostgreSQL well enough to be up and running within minutes, and as I lit the wood burner, I entered the first few pieces of information. For example, starting time, how many wood pieces I used.
It will be interesting to see after a few wood-burning sessions where I’m averaging out. That way I might be able to estimate the wood usage for the end of this year.
For the oil part, I already got a few data points, six in total. And I see an average oil usage of about 13 liters per day. Which would average out to about 400 liters per month. Not exactly cheap, to be honest, but we are currently in full winter mode. The temperature drops at night to below 0°C and sometimes is 0°C or lower the whole day.
It will be interesting to see how the wood burning affects the oil usage. You can find all stats over at: https://life-new.phaidenbauer.com/housing-costs-overview?hid=3
Comments
Normally I start with a simple Google Sheet, but I’m now tracking so much (data from my Apple Watch, gym visits, weights lifted, many things in the house), it feels easier to put it into something I’m already working with daily. It also makes it easy to build a quick page (recently trying out Astro.js) to visualize them. Though it would be nice to get some easy diagrams on some of the data, which would clearly be easier in a Sheet or Excel doc 🙈.
For more extensive insights, I use Grafana, pulling data from a remote Postgres table (where all the stuff is stored that needs to be accessed from everywhere), a local Postgres table with some home automation information, and an InfluxDB where Home Assistant (the open-source home automation I use) pushes into. It’s probably a mess, but at least it’s a mess I can query somewhat easily.
I should probably learn how to use a Postgre database since I’ve got the SQL chops, and I bet I could do much better analysis than what I currently do with Excel.
I’ve used MySQL/MariaDB in the past, but was introduced to PostgreSQL by my current employer. As far as I know, it’s the one SQL implementation that is as close to the standard as possible while providing a whole load of extra features. Not that I use any of those, but it’s nice knowing what it could do.
It’s crazy how scalable it is, seeing the loads of data we process every day. I think one of our largest tables is now well over 500 gigabytes and in the billions when it comes to row entries (about 20+ or so columns), but accessing it just takes mere milliseconds.
@phaidenbauer Ah I see. Makes sense! You can produce more outputs from it than if in Excel.
Damn - 500GB pg db?! That’s crazy
@jasonleow Yup, those 500 GB are only one table. I think our whole database spread across teams is now over a terabyte and still as fast as when I joined the company. It always breaks my mind a bit, looking into the whole picture.
Interesting… why postgres instead of just a normal google sheet?