Category: Computing

Learning Rust by (re)writing a prometheus exporter

As part of my self hosted infrastructure I’ve got prometheus setup gather metrics from various sources, and Grafana to visualize them. My TED5000 gives me power usage information for the whole house, and my thermostat provides temperature data.

When using prometheus, you need exporters to provide metric data. There is an existing TED5000 exporter that I’m using, but there wasn’t one that I found for the thermostat – so I created one. The initial implementation was in python, and this worked fine. However I’d see in my pi-hole dashboard that lookups of the thermostat name were high in the stats (22000+ lookups per 24hr period). Looking at the logs, it seems every 15 second four lookups would happen, two pairs (A and AAAA) separated by 2 seconds. I suspect this was a side effect of the radiotherm library I was using in my code.

The exporter is very simple, it’s just a webserver that responds by making a web request to the thermostat, and responding with the data formatted for prometheus. The response payload looks like:

I figured that this was a good opportunity for me to learn Rust with a practical project.

The first thing I did was get myself setup to compile Rust code. I did this using a docker container as inspired by my previous post.

At this point I was able to create my first hello world rust code. I went from printing “hello” to following a tutorial to create a very simple webserver – which will be sufficient for a prometheus exporter.

I then started to learn about cargo and the crates.io site. It turns out there are a few prometheus crates out there, I found one that looked like a good match but after looking at it in more details I decided it was a lot more code and capability than I was looking for. Consider again the very simple response payload above, I really need very little help from a library.

I located a tutorial on reading data from a URL in Rust. This tutorial was less complete as it made assumptions you had more knowledge of managing crates than I did at the time. In order to take the code snippet and get working code you needed to add two lines to your Cargo.toml file. Furthermore, the code makes use of the blocking feature in the reqwest library which is not on by default. Using crates.io you can find the libraries (error-chain, reqwest) and details on how to configure them. This ends up being what you need:

At this point I now have two samples of code. One which is a single threaded web server, and a second which can read from a URL (the thermostat) and parse out the JSON response. A little bit of hacking to figure out return syntax in Rust and I’ve managed to smash these together and have a basic exporter working.

The existing exporter runs as a container on my server, so all that remains is to wrap this Rust code up in a container and I’ve got a complete replacement.

Looking at the binary I’d been running with cargo run I was a little surprised to see it was 60MB, I was quick to rationalize that this was in the debug tree. Compiling a release version (cargo build --release) resulted in a much smaller binary (8.5MB). This size reduction sent me off to see how small a container I could easily create.

Two things I want: a) a multi-stage build Dockerfile b) a static binary that will run in a scratch image. Luckily this is well travelled ground and is concisely explained in this blog post for creating small rust containers.

The result was a 12MB docker image that only contains the one binary that is my code.

The previous python based implementation built on an alpine base image was 126MB in size, so a 10x reduction in size – plus this new image runs without privilege (ie: as a user). This means this container has a very small attack surface. It appears to use %0.01 CPU vs. the previous %0.02. You can check the complete code out on github.

My pi-hole is showing me that it is still one of the higher names resolved with 11520 lookups in a 24hr period. This maps out to 24 hours * 60 minutes * 4 (every 15 seconds). I’ve improved on the DNS lookups, but it still feels like I can further improve this with a some clever coding.

Using Docker to isolate development environments

In terms of modern programming languages, I would pick golang as a great language to use. To me, it feels a little bit like a modernized C and has a nice approach to building single file binaries. It has a module system, type safety and garbage collection.

I really dislike the way that you install golang onto your system and the way you end up managing that install with environment variables and the like. It’s not a terrible install story, but I don’t like polluting my development laptop with cruft. Multiple versions make this more annoying.

Docker on the other hand, despite it’s flaws, is worth having installed. It allows you to run lots of different stuff without needing to make a big commitment about the install. So instead of re-imaging my machine to start fresh, I just purge the containers and start again. Another benefit is that it’s relatively easy for me to point someone else at my configuration and for them to re-use it nearly directly.

Getting a docker image that will persist state and make it trivial to compile golang programs turns out to be very easy

A couple of things to note. I’m using a host mounted volume – specifically the current directory that I issue the docker create in. From inside the container it is mapped to /data. I’ve also named the container, making it easy for me to re-run/attach to it for future compiles.

Edit – running with -u is a good idea to make docker run as the right user (you). This will mean that files created by that container on the mounted host volume are owned by you.

As an example, here is how I’d go about compiling a github project that is in golang.

How slick is that? My development machine needs docker and git installed. The rest of this environment is entirely inside the docker container.

Let me now demonstrate persistence of the container from run to run.

Thus if I happen to need a utility which isn’t installed in the base golang bullseye image, it’s easy for me to install. Also, from run to run – I have persistence of the changes I’ve made to my named image.

LIRC vs ir-keytool

Related to my recent IoT hacking, what started me down this path is the long term annoyance of my X10 lighting being unreliable. X10 has always been problematic due to it’s use of power line communication, this has gotten worse as we add more and more noisy electronic devices that cause additional feedback onto the house wiring.

With the X10 light switch I had an IR-543 which mapped IR (infra red) and the rest of my home theater gear is all IR controlled, so a single remote could control everything including the lights. Another nice feature of the X10 light switch I had was soft on / soft off – meaning that when you turned the lights off they would dim down to off, and the same for on. At the start of a movie this is pretty nice.

Of course with a wifi enabled light switch, how do I get IR control? This seemed like a good reason to DIY a solution and build an IR controller / repeater based on a Raspberry Pi. I found that it’s relatively easy to control Tasmota devices with curl, so I was able to easily turn the lights on or off using a simple program. I was pleased to discover that the new light switch also had the soft on / soft off behaviour.

To build an IR device on Linux, I first thought of LIRC as I’ve used this in the past. As I dug deeper, it seems the LIRC project is quite dormant and I was fighting with a lot of stale tooling. I was succeeding in getting something working with the various remotes I wanted to use but it felt like it was a lot of work. Then a friend mentioned ir-keytable to me which led me to the more modern IR control in Linux solution.

The short version of the story is that the ir-keytable support is in a similar state as the LIRC work. I believe this boils down to the fact that IR control is still very niche, and there are lots of hardware variables due to many different remote controls. If you want to do something simple: receive IR input to control a linux machine, then ir-keytable is the way to go. More complex situations may require LIRC. Both approaches have their challenges but ir-keytable is the more modern solution.

The rest of this article will be about getting ir-keytable going on Raspberry Pi OS with a TSOP4838 IR receiver. For my application I have a more complex set of requirements so I’ll be continuing with an LIRC based solution, but more on that another time.

Continue reading “LIRC vs ir-keytool”