Grafana is a powerful self-hosted tool that converts complex metric data into clear, interactive dashboards and real-time charts. Best of all, you can install Grafana on multiple platforms via Docker, which is not only fast and clean, but also has nothing to do with the operating system. This article will teach you step by step how to install Grafana using Docker and show how to use it to monitor system metrics and visualize real-time data efficiently.

Table of contents

• Install Grafana using Docker
• Connect data sources and visualize metrics
• Using a pre-built dashboard

Install Grafana using Docker

Before installing Grafana, make sure your system has Docker installed.

First, run the following command to pull the Grafana image:

 <code>sudo docker pull grafana/grafana</code> 

Next, start a new Grafana container in the background and map it to port 3000:

 <code>sudo docker run -d -p 3000:3000 --name=grafana grafana/grafana</code> 

After the container is started, open the browser and visit http://ipnx.cn/link/4a914e5c38172ae9b61780ffbd0b2f90 to enter the Grafana interface. Log in with the default username and password (both admin ):

After clicking to log in, the system will prompt you to change your password to improve security. You can choose to update and submit a new password (recommended), or you can skip this step to continue using the default credentials:

After successfully logging in, you will see the following main interface:

Related Reading : In addition to Grafana, you can also use these tools to monitor your Linux system.

Connect data sources and visualize metrics

Grafana supports monitoring of multiple services and containers from a single dashboard. You can add multiple data sources (such as Prometheus, MySQL, MongoDB, etc.) and display data through preset or custom dashboards.

Let's use Prometheus as an example to configure it. First install and run Prometheus through Docker:

 <code>sudo docker pull prom/prometheus</code> 

Start the Prometheus container with the following command:

 <code>sudo docker run -d -p 9090:9090 --name=prometheus prom/prometheus</code> 

After the container is run, you can view the Prometheus web interface by visiting http://ipnx.cn/link/825ab77fe4eb3419db2fc9c6ceff7146 .

Add data source

When Grafana and Prometheus are both running normally, you can start adding the first data source:

Select the data source type you want to add and fill in the URL, for example http://ipnx.cn/link/825ab77fe4eb3419db2fc9c6ceff7146. After completion, click Save & Test . If you see the prompt Successfully queried the Prometheus API , it means that the connection is successful:

Create your first dashboard

After the connection is successful, you can create a dashboard and use components such as graphics and charts to visualize the metric data. Click the Create Dashboard button or New -> New dashboard to start creating:

Then click the Add a new visualization button to start adding visual components:

Then select Prometheus as the data source:

Select any available metrics, such as process_cpu_seconds_total, and click the Run Queries button to view the data chart:

You can also continue to add more visual components (such as CPU usage, memory usage, etc.) and adjust the layout by dragging and dropping.

In this way, you can build a real-time, interactive system or application data view for easy monitoring and analysis.

Using a pre-built dashboard

Grafana supports importing community-provided dashboards. For example, you can visit the official Grafana website to find and use ready-made dashboards shared by others:

To use these prebuilt dashboards, just copy the ID of any dashboard from the official website and paste it in Grafana's import interface.

Now that Grafana has been deployed, you can explore more advanced visualization functions, install practical plug-ins, or integrate them with tools such as Prometheus, MySQL, Elasticsearch, etc. to create a complete observability system. You can also try running other GUI applications in Docker to build a more interactive and flexible working environment.

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