How to Integrate Grafana with Home Assistant
This post was written by Kennedy Gichuki. Scroll down for the author’s bio.
This post covers how to get started with Home Assistant and Grafana, including setting up InfluxDB and Grafana with Docker, configuring InfluxDB to receive data from Home Assistant, and creating a Grafana dashboard to visualize your data. It provides a comprehensive guide for real-time monitoring and analysis of Home Assistant data.
In this tutorial, you’ll learn how to integrate Grafana with Home Assistant using InfluxDB. You’ll learn how to install and configure InfluxDB, how to install and configure Grafana, and how to create a Grafana dashboard to visualize data from Home Assistant.
Definitions: Grafana, Home Assistant, InfluxDB
Home Assistant is an open-source home automation platform that enables control and monitoring of various devices using a web interface or mobile app. With support for diverse devices and protocols, it’s highly customizable, allowing users to integrate new devices and protocols through custom components.
Grafana is a versatile open-source analytics and monitoring platform that offers powerful data visualization capabilities. It supports a diverse array of data sources, including popular databases like InfluxDB, Elasticsearch, Prometheus, MySQL, PostgreSQL, and more. With its extensibility through plugins, Grafana allows users to easily incorporate new data sources for enhanced analytics and monitoring.
InfluxDB is an open source time series database designed for storing and querying time series data. It offers support for a wide range of data types, including integers, floats, strings, booleans, and others. Additionally, InfluxDB provides the flexibility to incorporate new data types through the use of plugins.
Home Assistant integrates with Grafana, an analytics and monitoring platform, to visualize and analyze data. This data can be collected and stored in InfluxDB, an open source time series database, which allows for efficient querying of timestamped data. Together, these tools enable users to control and monitor their smart home devices through Home Assistant while visualizing and analyzing the data in real-time using Grafana and InfluxDB.
Set up InfluxDB for Home Assistant data visualization
Step 1: Install and configure InfluxDB
First, you’ll need to install InfluxDB on your machine. You can find instructions for your specific operating system on the InfluxDB website.
After installing InfluxDB, you’ll need to configure it to accept data from Home Assistant. You can do this by creating a new InfluxDB database and user account.
Open the InfluxDB CLI by running influx in your terminal.
Create a new database by running the following command:
CREATE DATABASE homeassistant
Create a new user account by running the following command:
CREATE USER homeassistant WITH PASSWORD 'yourpassword'
Grant permissions to the new user account by running the following command:
GRANT ALL ON homeassistant TO homeassistant
Exit the InfluxDB CLI by running exit in the terminal.
Step 2: Install the InfluxDB integration in Home Assistant
Next, you’ll need to install the InfluxDB integration in Home Assistant. You can do this by navigating to the Home Assistant web interface, selecting Settings from the sidebar, and then selecting Integrations. From there, you can search for InfluxDB and follow the prompts to configure the integration.
Read more instructions here.
When configuring the InfluxDB integration, be sure to enter the database name, user account, and password that you created in the previous step.
Step 3: Send data from Home Assistant to InfluxDB
After configuring the InfluxDB integration in Home Assistant, data should start flowing from Home Assistant to InfluxDB automatically. To verify that data is being sent, you can log in to the InfluxDB CLI by running influx in your terminal and then running the following command:
USE homeassistant SELECT * FROM "homeassistant"."autogen"."*" LIMIT 10
This will display the first ten data points in the Home Assistant database.
Install Grafana using Docker
If you prefer to run Grafana using Docker instead of installing it on your local machine, you can follow these steps.
Step 1: Install Docker
If you haven’t already, you’ll need to install Docker on your machine. You can find instructions for your specific OS on the Docker website.
Step 2: Pull the Grafana Docker Image
Once Docker is installed, you can pull the Grafana Docker image by running the following command in your terminal:
docker pull grafana/grafana
This will download the latest version of the Grafana Docker image to your machine.
Step 3: Run the Grafana Docker container
To start a new Grafana Docker container, run the following command in your terminal:
docker run -d -p 3000:3000 --name=grafana grafana/grafana
This will start a new container with the name grafana and map port 3000 of the container to port 3000 of your local machine.
Step 4: Access the Grafana web interface
Once the container is running, you can access the Grafana web interface by navigating to http://localhost:3000 in your web browser.
Step 5: Create a Grafana account and connect data sources
When you first access the Grafana web interface, you’ll be prompted to create a new user account. After creating your account, you can start configuring Grafana by adding a data source and creating dashboards, as described in the previous steps.
Step 6: Configure Grafana
After installing Grafana, you’ll need to configure it to connect to the InfluxDB database. You can do this by following these steps:
Open the Grafana web interface by navigating to http://localhost:3000 in your web browser. Log in to Grafana using the default username admin and password admin.
- Click on the Configuration icon in the sidebar and select Data Sources.
- Click on the Add data source button and select InfluxDB. Enter the following information in the InfluxDB details section:
HTTP method: GET
- Click on the Save & Test button to save the data source and test the connection to InfluxDB.
Step 7: Create a Grafana dashboard
After configuring the InfluxDB data source in Grafana, you can create a new dashboard to visualize the Home Assistant data. To do this, follow these steps:
Click on the Create icon in the sidebar and select Dashboard.
Click on the Add new panel button and select Graph.
Click on Panel title.
In the Query tab, select the InfluxDB data source that you configured in Step
Enter your InfluxDB query in the Query editor. For example, you might enter a query like this to display the temperature from a temperature sensor:
SELECT mean("value") FROM "temperature" WHERE ("entity_id" = 'sensor.temperature') AND $timeFilter GROUP BY time($__interval)
This query selects the average temperature value from the temperature measurement for the sensor.temperature entity in Home Assistant and groups the results by time interval.
Click on the Apply button to save the query and display the results.
Customize your panel as desired by selecting different visualization options, adding legends and annotations, etc.
Click on the Save icon in the toolbar to save the panel to your dashboard.
You can repeat these steps to add more panels to your dashboard, using different InfluxDB queries to display different data.
Step 8: View your dashboard
After creating your Grafana dashboard, you can view it by navigating to http://localhost:3000/dashboards in your web browser and selecting the dashboard from the list. You should now see a visual representation of your Home Assistant data in Grafana!
Conclusion: Home Assistant and Grafana
Getting started with Home Assistant and Grafana requires installing InfluxDB and Grafana on your machine, configuring InfluxDB to accept data from Home Assistant, installing the InfluxDB integration in Home Assistant, configuring Grafana to connect to the InfluxDB database, and creating a Grafana dashboard to visualize your data. With these tools and techniques, you can easily monitor and analyze your Home Assistant data in real-time.
About the author
This post was written by Kennedy Gichuki. Kenny is a developer and tech writer who enjoys Python and cloud infrastructure. After getting his Azure fundamentals, he has continued learning cloud tech and is now pursuing Machine learning. When not writing, Kenny can be found exploring hills and learning about the best overland vehicles.