Zach Peters

Carbon dioxide monitoring with Rust, InfluxDB and

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tags: #good-articles title: Carbon dioxide monitoring with Rust, InfluxDB and Grafana author: url:

Carbon dioxide monitoring with Rust, InfluxDB and Grafana

Blog Post

Slack notification for air quality Slack notification for air quality

Somehow this drew more attention than I expected, and people wanted to know how it works. So here is a quick summary.

The other day, when my colleagues had a long discussion about how to properly air out a room by arguing about miscalculated water vapor data, I thought of one of my side projects that could actually be useful in our office.

It started a while ago because I wanted to learn Rust (a very beautiful programming language btw.) and also wanted to revisit InfluxDB and Grafana, monitoring solutions I had introduced for a customer project two years ago.

So I wrote a tiny Rust program which reads the CO2 value from a sensor connected to a Raspberry Pi. All it does is reading the register values and writing them to stdout:

K30 CO2 sensor module K30 CO2 sensor module

extern crate byteorder;
extern crate serial;

use std::io::{Read, Write};
use byteorder::{BigEndian, ByteOrder};

fn main() {
    let mut port = serial::open("/dev/ttyS0").unwrap();

    port.write(&[0xFE, 0x44, 0x00, 0x08, 0x02, 0x9F, 0x25])
        .expect("💩 unable to write command!");

    let mut buf = vec![0; 7];
    port.read_exact(&mut buf).expect("💩 unable to read sensor data!");

    let co2 = BigEndian::read_u16(&[buf[3], buf[4]]);
    println!("{}", co2);

$ cargo build --release
  Finished release [optimized] target(s) in 17.91 secs

$ ./target/release/k30

Everything else I needed was already there:


[Telegraf]( is a small agent developed by InfluxData which is able to regularly [execute]( a process and store its output into InfluxDB.

### /etc/telegraf/telegraf.d/co2.conf

  commands = ["/usr/local/bin/co2"]
  timeout = "1s"
  name_override = "co2"
  data_format = "value"
  data_type = "integer"


[influxdb]( is a time series database with a powerful query language. I then used [moving-average]( which helps to even out spikes.

$ curl -s "http://localhost:8086/query?db=telegraf" \
  --data-urlencode "q=SELECT MOVING_AVERAGE(value, 3) FROM co2 WHERE time > now()-2m"  | jq .

  "results": [
      "statement_id": 0,
      "series": [
          "name": "co2",
          "columns": [
          "values": [
            ["2018-02-02T11:05:50Z", 764 ],
            ["2018-02-02T11:06:00Z", 760.3333333333334 ],
            ["2018-02-02T11:06:10Z", 757.3333333333334 ],
            ["2018-02-02T11:06:20Z", 755 ],
            ["2018-02-02T11:06:30Z", 754.6666666666666 ],
            ["2018-02-02T11:06:40Z", 754.6666666666666 ],
            ["2018-02-02T11:06:50Z", 754.6666666666666 ],
            ["2018-02-02T11:07:00Z", 754.3333333333334 ],
            ["2018-02-02T11:07:10Z", 753.6666666666666 ]


[Grafana]( is a tool to visualize your data. It comes with basic support functionality for alerts and can send [slack notifications](
via [incoming web hooks]( And it make it very easy to [upload images]( of your charts to your cloud storage or WebDAV, so you can immediately see what’s wrong, or share the graph without any singe-page app hassle.


Playing with Rust is fun and deploying a monitoring solution is a lot easier now than it was two years ago.**

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