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Prometheus exporter for enviro module by Pimoroni
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• About the Project
  • Built With
• Getting Started
  • Prerequisites
  • Installation
• Usage
• Docker
• Roadmap
• Contributing
• License
• Contact
• Acknowledgements

• Pimoroni/enviro-python
• Prometheus
• Grafana

To get the prometheus enviro-exporter up and running I'm assuming you already have Prometheus and Grafana running somewhere. Note: I wouldn't recommend running Prometheus on a Raspberry Pi (using a local SD card) as this could drastically reduce the lifetime of the SD card as samples are written quite often to disk.

• Python3
• To run the enviro-exporter you need to have the enviro-python library by Pimoroni installed:

curl -sSL https://get.pimoroni.com/enviro | bash

Note Raspbian Lite users may first need to install git: sudo apt install git curl

1.Install the package from pypi

pip3 install enviro_prometheus_exporter

3.Install as a Systemd service

SERVICE_FILE=https://raw.githubusercontent.com/tijmenvandenbrink/enviro_exporter/master/contrib/enviro-exporter.service
sudo curl ${SERVICE_FILE} -o /etc/systemd/system/enviro-exporter.service
sudo chmod 644 /etc/systemd/system/enviro-exporter.service
sudo systemctl daemon-reload

4.Start the enviro-exporter service

sudo systemctl start enviro-exporter

5.Check the status of the service

sudo systemctl status enviro-exporter

If the service is running correctly, the output should resemble the following:

pi@raspberrypi:/usr/src/enviro_exporter $ sudo systemctl status enviro-exporter
● enviro-exporter.service - Enviro-exporter service
   Loaded: loaded (/etc/systemd/system/enviro-exporter.service; disabled; vendor preset: enabled)
   Active: active (running) since Fri 2020-01-17 14:13:41 CET; 5s ago
 Main PID: 30373 (python)
    Tasks: 2 (limit: 4915)
   Memory: 6.0M
   CGroup: /system.slice/enviro-exporter.service
           └─30373 /usr/bin/python /usr/src/enviro_exporter/enviro_exporter.py --bind=0.0.0.0 --port=8000

Jan 17 14:13:41 wall-e systemd[1]: Started Enviro-exporter service.
Jan 17 14:13:41 wall-e python[30373]: 2020-01-17 14:13:41.565 INFO     enviro_exporter.py - Expose readings from the Enviro+ sensor by Pimoroni in Prometheus format
Jan 17 14:13:41 wall-e python[30373]: Press Ctrl+C to exit!
Jan 17 14:13:41 wall-e python[30373]: 2020-01-17 14:13:41.581 INFO     Listening on http://0.0.0.0:8000

6.Enable at boot time

sudo systemctl enable enviro-exporter

eviro-exporter --help for a list of available flags and options:

Usage: enviro-exporter [OPTIONS]

Options:
  -b, --bind TEXT     Specify alternate bind address [default: 0.0.0.0]
  -p, --port INTEGER  Specify alternate port [default: 8000]
  -f, --factor FLOAT  Factor to compensate temp [default: 2.25]
  -d, --debug         Turns on more verbose logging, prints output [default:
                      False]

  -i, --influxdb      Post sensor data to InfluxDB [default: False]
  -l, --luftdaten     Post sensor data to InfluxDB [default: False]
  -e, --enviro        Set this if you have an Enviro (not Enviro+) so don't
                      have gas and particulate sensors

  -s, --pms           Enable pms5003 data collection [default: False]
  -v, --version       Prints version number and exits [default: False]
  --help              Show this message and exit.

Add the flag --pms to your run command if you don't have a pms. This will stop particulate data collection.

If you are using an Enviro (not Enviro+) add --enviro to the command line (in the /etc/systemd/system/enviro-exporter.service file) then it won't try to use the missing sensors.

So now we've setup the Prometheus enviro-exporter we can start scraping this endpoint from our Prometheus server and get a nice dashboard using Grafana.

If you haven't setup Prometheus yet have a look at the installation guide here.

Below is a simple scraping config:

# Sample config for Prometheus.

global:
  scrape_interval:     15s # By default, scrape targets every 15 seconds.
  evaluation_interval: 15s # By default, scrape targets every 15 seconds.
  # scrape_timeout is set to the global default (10s).

  # Attach these labels to any time series or alerts when communicating with
  # external systems (federation, remote storage, Alertmanager).
  external_labels:
      monitor: 'external'

# Load and evaluate rules in this file every 'evaluation_interval' seconds.
rule_files:
  # - "first.rules"
  # - "second.rules"

# A scrape configuration containing exactly one endpoint to scrape:
# Here it's Prometheus itself.
scrape_configs:
  # The job name is added as a label `job=<job_name>` to any timeseries scraped from this config.
  - job_name: 'prometheus'

    # Override the global default and scrape targets from this job every 5 seconds.
    scrape_interval: 15s
    scrape_timeout: 15s

    # metrics_path defaults to '/metrics'
    # scheme defaults to 'http'.

    static_configs:
    - targets: ['localhost:9090']

  - job_name: node
    # If prometheus-node-exporter is installed, grab stats about the local
    # machine by default.
    static_configs:
    - targets: ['localhost:9100']

    # If environment-exporter is installed, grab stats about the local
    # machine by default.
  - job_name: environment
    static_configs:
    - targets: ['localhost:8000']
      labels:
        group: 'environment'
        location: 'Amsterdam'
        
    - targets: ['newyork.example.com:8001']
      labels:
        group: 'environment'
        location: 'New York'

I added two labels to the targets group: environment and location: SomeLocation. The Grafana dashboard uses these labels to distinguish the various locations.

I published the dashboard on grafana.com. You can import this dashboard using the the ID 11605. Instructions for importing the dashboard can be found here.

There is a Dockerfile available if you'd like to run as a docker container.

1.Building

docker build -t enviro-exporter .

2.Running

docker run -d enviro-exporter -d -p 8000:8000 --device=/dev/i2c-1 --device=/dev/gpiomem --device=/dev/ttyAMA0 enviro-exporter

See the open issues for a list of proposed features (and known issues).

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Distributed under the MIT License. See LICENSE for more information.