Although the PiGlow visualisation of CPU usage was pretty, we reckoned we could go a couple of steps further and integrate a much more complete tangible solution – a hardware-driven load monitor dashboard.
Made of cardboard.
This was to be driven by two high torque servos (Ben had them lying around) which would rotate according to whichever performance indicator we chose. Servos are not, of course, very good pointers so with a trusty craft knife to the fore we re-purposed some Pi packaging into a cardboard user interface.
In my last post I described how I set up a 5-strong Raspberry Pi Docker swarm. It wasn’t long before I realised I wanted some ambient way to see how they were performing which a) didn’t involve staring at a screen and b) would wind up the cat.
Luckily my friend Ben was round and he’s quite into tangible stuff so after rummaging in a few dusty boxes for inspiration we found a PiGlow and wondered if that would do the trick.
Do you use git to manage your site and or server files? In my opinion, this is undoubtably a good way to run things but you need to make sure it’s secure. Just try going to yoursite.com/.git/config. If you haven’t secured your server properly, you will see the configuration file for your git repository. Not good, huh? Not only could an attacker reveal lots of information about your code base including where the upstream server is, I believe they could possibly get the entire source. This would allow the attacker to see exactly how the site works and be able to exploit it very easily.
Now, the good news. It’s an easy fix!
We all love drones. We all love cake. And we all love Raspberry Pi. What better way to spend an afternoon than to kick up at my mate Ben‘s house, borrow his faster internet and combine all of those things.
My father, Ben Anderson plays with numbers. As his Twitter bio says “big data, small data, open data, any data”. He works with R a lot and has been persuading me to take a look at it. I’ve held off until now because I’m all for analysing data in real time (primarily using delightful JS libraries such as Chart.js and D3.js). As far as I understood it, R is geared towards static data analysis and because of that, is able to utilise the hardware it runs on to optimise computations. Dad has an SSD in his Mac which reduces the time to load data substantially, but he also makes use of the R package data.table. This library makes manipulation of data ridiculously fast as it stores it all in RAM.