Grafana Panel Plugin Tutorial: A Step-by-Step Guide

Hey everyone, welcome back! Today, we’re diving deep into the awesome world of Grafana panel plugins . If you’ve been using Grafana to visualize your data, you know how powerful it is. But what if the default panels just don’t cut it for your specific needs? That’s where custom panel plugins come in, guys! We’re going to walk through creating your very own Grafana panel plugin from scratch. This tutorial is designed to be super comprehensive, so whether you’re a seasoned developer or just getting your feet wet with Grafana customization, you’ll be able to follow along. We’ll cover everything from setting up your development environment to building and testing your plugin. So, grab your favorite coding beverage and let’s get started on building some seriously cool visualizations!

Understanding Grafana Panel Plugins

Alright, so what exactly is a Grafana panel plugin , you ask? Think of Grafana as a blank canvas for your data. The default panels – like graphs, stat panels, and tables – are like pre-made brushes and colors. They’re fantastic for most use cases, but sometimes you need a special paintbrush to create a masterpiece. A Grafana panel plugin is essentially a custom component that you can add to your Grafana dashboards to display data in a unique way. It allows you to go beyond the standard visualizations and create something tailored precisely to your data’s characteristics and your reporting needs. This is a game-changer, especially for niche industries or complex data relationships where standard charts might fall short. Custom panel plugins can be as simple as a slightly modified version of an existing panel or as complex as a fully interactive 3D data representation. The key benefit here is flexibility and customization . You’re not limited by what Grafana offers out-of-the-box; you can build exactly what you envision. The Grafana ecosystem is built to be extensible, and plugins are the primary way to achieve this. They can fetch data, process it, and render it using any web technologies you’re comfortable with, typically JavaScript, React, and modern web frameworks. This means you can leverage your existing development skills to enhance your data visualization capabilities within Grafana. We’ll be focusing on building a simple yet illustrative plugin in this tutorial, but understanding this core concept of extensibility is crucial for appreciating the full potential of Grafana. So, in a nutshell, panel plugins empower you to extend Grafana’s visualization capabilities, offering unparalleled control over how your data is presented to your users. This tutorial aims to demystify the process, making it accessible and actionable for you, the developer looking to push the boundaries of data visualization.

Setting Up Your Development Environment

Before we start coding our awesome Grafana panel plugin , we need to get our development environment all set up. Don’t worry, it’s not as intimidating as it sounds! The primary tool you’ll need is Node.js and npm (or Yarn, if that’s your jam). Grafana plugins are typically built using modern JavaScript frameworks, with React being a very popular choice. If you don’t have Node.js installed, head over to the official Node.js website and download the latest LTS (Long Term Support) version. Once Node.js is installed, npm will be installed along with it. You can check if they’re installed correctly by opening your terminal or command prompt and typing node -v and npm -v . You should see version numbers printed out. The next crucial step is to install the Grafana plugin SDK. This SDK provides all the necessary tools, templates, and utilities to streamline the plugin development process. To install it globally, run the following command in your terminal: npm install -g @grafana/create-plugin . This command installs the create-grafana-plugin scaffolding tool, which is super handy for generating the basic structure of your plugin. Think of it as a starter kit that sets up all the boilerplate code, build configurations, and necessary dependencies for you. It’s like having a pre-built house that you can then customize to your heart’s content. Once the SDK is installed, you can create a new plugin project by navigating to the directory where you want your plugin to reside in your terminal and running: npx @grafana/create-plugin . This command will prompt you with a few questions, such as the name of your plugin, its description, and the type of plugin you want to create. For this tutorial, we’ll be creating a panel type plugin. The tool will then generate a new directory with your plugin’s name, pre-populated with all the essential files and folders. You’ll find a src directory for your source code, a package.json file for managing dependencies, and various configuration files for building and testing. It’s crucial to have this setup correct , as it forms the foundation for everything else we’ll do. Make sure you have Git installed too, as it’s often used for version control and is a good practice for any development project. If you encounter any issues during this setup phase, the official Grafana documentation is your best friend, and there are tons of helpful resources online. Getting this environment right now will save you a ton of headaches later, so take your time and ensure everything is running smoothly before proceeding.

Creating Your First Panel Plugin

With our development environment all spiffed up, it’s time to actually create our Grafana panel plugin ! We’ll start by using the create-grafana-plugin tool we installed earlier. Navigate to the directory where you want your plugin to live in your terminal and run the command: npx @grafana/create-plugin . This command will ask you a series of questions to scaffold your new plugin. Let’s walk through them:

1. What is the name of your plugin? (e.g., my-awesome-panel )
2. What is the description of your plugin? (e.g., A custom panel for awesome data visualization )
3. What type of plugin would you like to create? Choose panel .
4. Would you like to use React or plain JavaScript? For this tutorial, we’ll choose React as it’s very common and offers great tooling.
5. Would you like to add Grafana UI components? You can answer yes or no , but yes will give you access to pre-built Grafana components, which is nice.

Once you’ve answered these, the tool will generate a new directory with your plugin’s name. Let’s say you named it my-awesome-panel . You’ll cd into this directory: cd my-awesome-panel . Inside, you’ll find a src folder containing the main plugin code. The entry point for your panel is usually module.ts or plugin.ts , and the actual React component is often in a file like SimplePanel.tsx (or similar). Your package.json file will have scripts for building and starting a development server. The most important ones for development are usually npm run dev (or yarn dev ) which watches your files and rebuilds the plugin on changes, and npm run build (or yarn build ) for creating a production-ready build. This initial scaffolding is super helpful because it sets up the build tools (like Webpack), linters, and basic structure for you. It means you don’t have to manually configure all of that, which can be a real pain. You’ll see files like GrafanaPlugin.tsx which is where the main logic for your panel resides, including how it receives data and configuration options. The module.ts file registers your panel with Grafana. The panel-options.ts file defines the configuration options that users will see in the panel editor on the right side of the Grafana dashboard. These options allow users to customize the appearance and behavior of your panel without needing to touch the code. For instance, you might add options for changing colors, text size, or specific display modes. This is where the real customization begins, as you define the user-facing controls for your plugin. Now, let’s take a quick look at the main React component, often found in SimplePanel.tsx . This is where you’ll write the JSX and logic to render your data. It receives props like options (your custom settings) and data (the data fetched from your data source). Mastering this component is key to creating your unique visualization. So, this is the initial setup. We have a functioning plugin structure, ready for us to inject our custom visualization logic! Pretty neat, right?

Understanding Plugin Structure and Data Flow

Let’s get real for a sec, guys. To build a killer Grafana panel plugin , you gotta understand how it all fits together and how data actually flows into your creation. The Grafana plugin architecture is pretty well-organized. When you create a plugin using @grafana/create-plugin , it sets up a standard structure. You’ll typically find a src directory, and inside that, you’ll have your main plugin file (like module.ts ), your panel component file (like SimplePanel.tsx ), and potentially a file for defining your panel’s options (like panel-options.ts ). The module.ts file is where your plugin is registered with Grafana. It tells Grafana that you have a new panel type available. The main thing it does is export a PanelPlugin class. This class is where you configure the plugin’s metadata, define its display name, and crucially, connect it to your React component. It’s also where you define the configuration options for your panel. The React component (e.g., SimplePanel.tsx ) is the heart of your visualization. This is where you’ll write the JSX code to render your data. Grafana passes data to this component via props. This data prop is usually an object that contains the time series data, table data, or other formats that your data source returns. You’ll also receive an options prop, which contains the configuration values set by the user in the panel editor. So, the flow is generally: Data Source -> Grafana Backend -> Panel Component -> Your Visualization . When a user adds your plugin to a dashboard, Grafana fetches data based on the query configured in the panel. This data, along with the user-defined options, is then passed to your React component. Your component’s job is to take this raw data and options and transform them into a meaningful visualization. Understanding this data prop is key . It’s often a DataFrame object, which is Grafana’s standard way of representing data. You’ll need to know how to iterate over the fields in a DataFrame to extract the values you need for your chart, table, or whatever cool thing you’re building. For example, if you’re drawing a graph, you’ll be looking for fields that represent time, x-axis values, and y-axis values. If you’re creating a table, you’ll be extracting column headers and row data. The options prop is also super important. This is where users can customize your plugin. You define these options in panel-options.ts (or similar). For example, you might have options for choosing a color, setting a threshold, or selecting a specific display mode. Your React component will read these options and adjust its rendering accordingly. This separation of concerns – the plugin registration, the data handling, the UI rendering, and the user configuration – makes the whole process manageable. By understanding how data flows from Grafana into your component and how options influence its behavior, you’re well on your way to creating truly dynamic and user-friendly panel plugins. It’s all about grabbing that data, interpreting it based on the user’s settings, and rendering it beautifully.

Developing the Visualization Component

Now for the really fun part, guys: developing the visualization component for your custom panel plugin! This is where you get to be creative and actually build the visual representation of your data. As we mentioned, the core of your panel plugin will be a React component. Let’s assume your main component file is src/SimplePanel.tsx . This component receives two primary props: data and options . The data prop is where Grafana sends the information fetched from your data source. This could be time-series data, tabular data, or other formats. The options prop contains all the custom settings that the user has configured in the panel editor. Your job is to take these inputs and render something meaningful. For a simple example, let’s say we want to display a custom message and the number of data points we received. Inside SimplePanel.tsx , you might have something like this:

import React from 'react';
import { PanelProps } from '@grafana/data';
import { SimpleOptions } from 'types'; // We'll define types later

interface Props extends PanelProps<SimpleOptions> {}

export const SimplePanel: React.FC<Props> = ({ options, data }) => {
  // Calculate the number of data points
  let dataPointsCount = 0;
  if (data && data.series && data.series.length > 0) {
    dataPointsCount = data.series.reduce((sum, series) => sum + series.fields.reduce((fieldSum, field) => {
      // Count non-null values in relevant fields (e.g., value fields)
      return field.type === 'number' ? fieldSum + field.values.length : fieldSum;
    }, 0), 0);
  }

  return (
    <div
      style={{
        width: '100%',
        height: '100%',
        display: 'flex',
        justifyContent: 'center',
        alignItems: 'center',
        fontSize: options.textSize,
        color: options.textColor,
      }}
    >
      <div>
        <p>Hello from my custom panel!</p>
        <p>Number of data points received: <strong>{dataPointsCount}</strong></p>
        <p>Display Text: <em>{options.textToShow}</em></p>
      </div>
    </div>
  );
};

In this snippet, we’re accessing options.textSize , options.textColor , and options.textToShow . These would be defined in your types.ts and module.ts files as customizable options. We’re also calculating dataPointsCount by iterating through the data.series and their fields. This is where you’d integrate any charting library you want – Chart.js, D3.js, Plotly.js, you name it! You’d use the data prop to feed information to that library and render your custom chart. The style attribute is used here for basic centering and sizing, ensuring your visualization fills the panel. Remember to always consider responsiveness – how your visualization looks on different screen sizes. Grafana’s panel container provides the dimensions, and you should use them to scale your visualization appropriately. For more complex visualizations, you might fetch data and process it before passing it to a rendering function. You could also incorporate UI elements from @grafana/ui to create interactive controls within your panel. The key takeaway is to leverage the data and options props effectively . The data prop is your raw material, and the options prop is your user’s toolkit for shaping the final output. Experiment with different libraries and rendering techniques to create the exact visualization you need. This is your canvas, so go wild!

Adding Customizable Options

One of the most powerful aspects of Grafana panel plugins is the ability to let users customize them directly from the dashboard interface. Nobody wants to dig into code every time they want to tweak a color or a label, right? This is where defining customizable options comes into play. These options appear in the panel editor sidebar on the right side of your Grafana dashboard, allowing users to easily configure your plugin without writing a single line of code. We define these options using Grafana’s PanelOptionsEditor component. Typically, this is done in a file like src/panel-options.ts or directly within your module.ts file where you register your plugin.

Let’s look at an example of how you might define options for our SimplePanel from the previous section. In your module.ts (or a dedicated panel-options.ts file), you’d add something like this:

import { PanelPlugin } from '@grafana/core';
import { SimplePanel } from './SimplePanel';
import { SimpleOptions } from './types';

export const plugin = new PanelPlugin<SimpleOptions>(SimplePanel).setPanelOptions((builder) => {
  return builder
    .addTextInput({
      path: 'textToShow',
      name: 'Text to Display',
      description: 'Enter the text you want to show in the panel.',
      defaultValue: 'Hello World!',
    })
    .addNumberInput({
      path: 'textSize',
      name: 'Text Size',
      description: 'Font size for the text.',
      defaultValue: 16,
    })
    .addColorPicker({
      path: 'textColor',
      name: 'Text Color',
      description: 'Color of the text.',
      defaultValue: '#000000',
    });
});

Here’s a breakdown of what’s happening:

• setPanelOptions((builder) => { ... }) : This is the method used to define your panel’s options. The builder object provides methods for adding various types of input fields.
• addTextInput({...}) : Adds a text input field. The path property (‘textToShow’) is crucial – it’s how your React component will access this option (e.g., options.textToShow ). The name is what the user sees, and defaultValue sets the initial value.
• addNumberInput({...}) : Adds a field for numerical input, perfect for sizes, counts, or thresholds.
• addColorPicker({...}) : Adds a user-friendly color picker.

Crucially, you also need to define the types for these options. This is usually done in a src/types.ts file:

export interface SimpleOptions {
  textToShow: string;
  textSize: number;
  textColor: string;
}

Make sure the path values in your module.ts match the property names in your SimpleOptions interface exactly. This linkage is essential for Grafana to pass the options correctly to your React component. By providing these intuitive controls, you make your plugin accessible to a wider audience and allow for dynamic dashboard configurations. Users can experiment with different settings to best represent their data, all without needing to be developers themselves. This makes your plugin far more valuable and user-friendly. Think about what aspects of your visualization might need tuning – colors, scales, labels, thresholds, display modes – and expose them as options. This is where good UX design meets data visualization!

Building and Testing Your Plugin Locally

Alright, you’ve built your visualization and added some slick options. Now, how do you actually see it in action and make sure it’s working correctly? Building and testing your plugin locally is a critical step in the development process. Grafana provides excellent tooling to make this as smooth as possible. First things first, you need to run your plugin in development mode. Navigate to your plugin’s root directory in your terminal and run:

npm run dev

(Or yarn dev if you prefer Yarn). This command does a few things: it compiles your plugin code, sets up a development server, and usually watches your files for changes. When you save a file, it will automatically recompile, making the development loop very fast.

Next, you need to tell your local Grafana instance about your plugin. The easiest way to do this is to copy your plugin’s dist folder (which is generated by the dev command) into your Grafana installation’s plugin directory. The exact location of this directory depends on your operating system and how you installed Grafana. Common locations include:

• Linux: /var/lib/grafana/plugins
• macOS: /usr/local/var/lib/grafana/plugins
• Windows: C:\Program Files\GrafanaLabs\grafana\data\plugins

Alternatively , and often more conveniently for development, you can configure Grafana to load plugins from a specific directory. In your grafana.ini configuration file (usually found in the conf directory of your Grafana installation), you can set the plugins path:

[paths]
  plugins = /path/to/your/grafana-plugins

Then, you would create a symbolic link or copy your plugin’s dist folder into this specified directory. For example, on Linux, you might do:

cd /path/to/your/grafana-plugins
ln -s /path/to/your/my-awesome-panel my-awesome-panel

After placing your plugin in the correct directory (or linking it), you’ll need to restart your Grafana server for it to detect the new plugin. Once Grafana restarts, you should be able to add your custom panel to a dashboard just like any other built-in panel. Go to an existing dashboard, click ‘Add panel’, and search for the name you gave your plugin (e.g., ‘My Awesome Panel’).

Testing involves:

1. Adding the panel: Ensure it appears in the list and can be added to the dashboard.
2. Configuring data: Connect it to a data source and set up a query. Verify that your visualization receives and displays the data correctly.
3. Testing options: Use the panel editor sidebar to change the options you defined (text, size, color, etc.) and confirm that the visualization updates as expected.
4. Error checking: Keep an eye on your browser’s developer console (usually accessible by pressing F12) for any JavaScript errors. The npm run dev command often outputs helpful messages in your terminal as well.

For production builds , you’ll use npm run build (or yarn build ). This command creates an optimized, minified version of your plugin in the dist folder, ready for deployment. You would then package this dist folder (along with any other necessary files like plugin.json ) and distribute it.

Iterative development is key . Make a small change, test it, repeat. This approach helps catch bugs early and ensures your plugin behaves as intended. Don’t be afraid to break things – that’s how you learn! And remember to check the Grafana server logs ( grafana.log ) for backend-related issues if any arise.

Deploying Your Plugin

So, you’ve built an awesome Grafana panel plugin , tested it thoroughly, and it’s looking great! Now, how do you share this gem with the world, or at least with your colleagues?

Deployment essentially means getting your plugin into the hands of other Grafana users. There are a few ways to go about this, depending on your needs:

1. Manual Deployment (Simple Sharing)

This is the most straightforward method, especially for internal use within your organization. You’ve already done the build step using npm run build (or yarn build ). This creates an optimized dist folder. To deploy manually:

1. Build your plugin: Run npm run build in your plugin’s root directory.
2. Locate the dist folder: This folder contains all the compiled JavaScript, CSS, and other assets your plugin needs.
3. Package: You might want to zip the dist folder along with your plugin.json file (which contains metadata about your plugin) and potentially a README file.
4. Distribute: Share this zip file with your colleagues.
5. Installation: Users will need to unzip the package into their Grafana server’s plugin directory (the same directory we discussed in the testing section, often data/plugins ).
6. Restart Grafana: As always, Grafana needs to be restarted for it to recognize the newly installed plugin.

Pros: Simple, no external services needed. Cons: Manual process, harder to manage updates.

2. Using Grafana’s Plugin Development Server (for development only)

While not for production, during development, you can use npm run dev which runs a local development server. This rebuilds the plugin on changes and makes it available to your local Grafana instance. This is fantastic for rapid iteration but obviously not a deployment strategy.

3. Publishing to Grafana Labs Plugin Catalog

If your plugin is useful for the wider Grafana community, you can submit it to the official Grafana Labs Plugin Catalog. This is the most professional way to distribute your plugin.

1. Prepare your plugin: Ensure it adheres to Grafana’s plugin guidelines. This usually involves having a clear README.md , proper licensing, and potentially end-to-end tests.
2. Create a GitHub repository: Your plugin source code needs to be hosted on GitHub.
3. Submit via the Grafana Plugin SDK: Use the npx @grafana/create-plugin tool (or other related Grafana tools) which guides you through the submission process. This involves creating a release tag on your GitHub repo.
4. Review process: Grafana Labs will review your plugin for quality, security, and adherence to standards.

Pros: Wide distribution, discoverability, official endorsement. Cons: Requires meeting specific standards, review process can take time.

4. Private Plugin Repositories

For larger organizations, you might set up your own private plugin repository or use Grafana Enterprise’s features for managing private plugins. This allows you to distribute plugins internally without going through the public catalog.

Regardless of the method , the core build artifact is the compiled code found in the dist folder after running npm run build . Packaging and distribution are key . Always include clear installation instructions, usually in a README.md file. Keep your plugin updated as Grafana evolves. New Grafana versions might introduce breaking changes or new features that your plugin could leverage. Staying current ensures your plugin remains compatible and useful. So, choose the deployment method that best suits your audience and requirements, and get your awesome visualizations out there!

Best Practices and Next Steps

Wow, guys, we’ve covered a lot of ground! We’ve set up our environment, created a plugin, understood the data flow, built a visualization, added options, and even talked about deployment. But before you go off and build the next big thing in data viz, let’s wrap up with some best practices and next steps to make your Grafana panel plugin development journey even smoother.

Best Practices:

• Keep it Simple: Start with a clear, focused purpose. Don’t try to build a Swiss Army knife on your first attempt. A plugin that does one thing exceptionally well is often more valuable than one that tries to do too much poorly.
• Leverage Grafana’s UI Kit: Use the @grafana/ui library whenever possible. This ensures your plugin has a consistent look and feel with the rest of the Grafana interface, improving user experience. Components like Button , Input , Select , and Graph (if applicable) are your friends.
• Handle Data Efficiently: Be mindful of how you process data. For large datasets, avoid doing heavy computations in the browser component if possible. Consider optimizations or leveraging backend data processing if your data source allows.
• Robust Error Handling: Implement checks for missing data, invalid configurations, or unexpected responses from data sources. Provide user-friendly error messages within the panel instead of just showing a blank space or a cryptic browser error.
• Write Clean, Readable Code: Use meaningful variable names, add comments where necessary, and follow established coding standards (like Airbnb’s JavaScript style guide, often enforced by linters). This makes your plugin easier to maintain and understand for yourself and others.
• Version Control (Git): Use Git from the very beginning. Commit often, write descriptive commit messages, and consider using feature branches for new developments.
• Documentation is Key: Always include a comprehensive README.md file explaining what your plugin does, how to install it, and how to configure it. Include screenshots if possible.
• Accessibility: Consider users with disabilities. Ensure your visualizations are keyboard-navigable and provide alternative text or descriptions where appropriate.

Next Steps:

• Explore More Advanced Features: Dive deeper into the Grafana plugin SDK documentation. Learn about custom data transformations, using Grafana’s PanelContext , building custom editors for options, and integrating with Grafana’s alerting system.
• Contribute to Existing Plugins: Look at the source code of popular open-source Grafana plugins on GitHub. Understanding how others solve complex problems can be incredibly insightful.
• Experiment with Different Data Sources: Test your plugin with various data sources (Prometheus, InfluxDB, Elasticsearch, SQL databases) to ensure its compatibility and identify potential data format issues.
• Build More Complex Visualizations: Try integrating with more sophisticated charting libraries or even develop entirely custom SVG or Canvas-based visualizations.
• Consider Performance: As your plugin grows, profile its performance. Use browser developer tools to identify bottlenecks and optimize rendering and data handling.
• Community Engagement: Engage with the Grafana community forums or Slack channels. Ask questions, share your work, and help others.

Building Grafana panel plugins is a rewarding way to extend your data visualization capabilities. It empowers you to tailor dashboards precisely to your needs. Keep experimenting, keep learning, and most importantly, have fun building amazing things with data! Thanks for following along, and happy visualizing!