Monitoring Apartment temperature & humidity with Raspberry Pi, Prometheus & Grafana

For quite some time, I had a spare Raspberry Pi lying around in my place. And one weekend I came up with idea to make my apartment "smarter". What I mean by saying "smarter" is tracking some metrics of my surroundings.

I have some experience in working with Prometheus and Grafana, so I decided to incorporate those tools into my solution. Yes, it does sound like overengineering simple task, you can probably get same results in much simpler way : ). But it was fun weekend project for me.

In this post I'll describe my setup for monitoring room temperature & humidity.

Hardware components

These are all the component, I used in my project:

• Raspberry Pi 3 Model B
• 16 GB microSD card
• DHT11 Temperature And Humidity Sensor
• Mobile phone charger, for powering Raspberry Pi

Connecting Sensor to Raspberry Pi

I connected Ground pin to the Ground of Raspberry PI, Data Pin to GPIO 14 pin, Vcc pin to 3.3V power supply pin.

Reading sensor data

For reading sensor data and feeding it to Prometheus, I chose DHT11_Python library, which is quite unstable, and sometimes does not return valid results, so you might get some gaps in your graphs.

Also I've created simple Flask API to serve metrics for Prometheus:

from flask import Flaskimport dht11import RPi.GPIO as GPIOGPIO.setwarnings(False)GPIO.setmode(GPIO.BCM)instance = dht11.DHT11(pin=14)app = Flask(__name__)@app.route("/metrics")def metrics():    dht11_data = ""    result = instance.read()    if result.is_valid():        dht11_data = f"""pihome_temperature {result.temperature}pihome_humidity {result.humidity}"""    return f"{dht11_data}", 200, {'Content-Type': 'text/plain; charset=utf-8'}if __name__ == "__main__":    app.run(host='0.0.0.0')

Prometheus configuration

To scrape metrics from my Flask API, I've added configuration to prometheus.yml:

global:    scrape_interval: 30sscrape_configs:    - job_name: 'pihome'      static_configs:        - targets: [pihome:5000]

Grafana Configuration

Then, in /etc/grafana/provisioning, I've added datasource configuration:

apiVersion: 1datasources:  - name: Prometheus    type: prometheus    url: http://prometheus:9090/    access: proxy    isDefault: true

It is also possible to add Grafana dashboards to provisioning folder as json files, so that you don't need to create new dashboard each time you re-deploy Grafana.

Connecting everything together

To make everything portable and easy to install, I packed my Flask API to Docker image and configured all services in docker-compose.yaml:

version: '3'services:  pihome:    image: pihome    build: .    restart: always    devices:      - "/dev/mem:/dev/mem"    privileged: true    ports:      - 5000:5000  prometheus:    image: prom/prometheus:v2.16.0    user: root    volumes:      - ./prometheus/:/etc/prometheus/      - /var/prometheus:/prometheus    command:      - '--config.file=/etc/prometheus/prometheus.yml'      - '--storage.tsdb.path=/prometheus'      - '--storage.tsdb.retention.time=30d'      - '--web.console.libraries=/usr/share/prometheus/console_libraries'      - '--web.console.templates=/usr/share/prometheus/consoles'    ports:      - 9090:9090    depends_on:      - pihome    restart: always  grafana:    image: grafana/grafana:6.6.2    depends_on:      - prometheus    ports:      - 80:3000    volumes:      - ./grafana/:/etc/grafana    restart: always

Results

I left my stack running for some time, to collect some historical data, and dashboard looked like this:

Git project

You can find my full configuration and code on Github: https://github.com/pdambrauskas/pihome