General

Decomposing a project using Nx - Part 1

Published Apr 8, 2021

Updated Feb 14, 2023

7 min read

This article was written over 18 months ago and may contain information that is out of date. Some content may be relevant but please refer to the relevant official documentation or available resources for the latest information.

Decomposing a project using Nx - Part 1

Working on a large codebase brings multiple challenges that we need to deal with. One of them is how to manage the repository structure and keep it as clean and maintainable as possible. There are multiple different factors that can be considered when talking about project maintainability, and one of them, that is fundamental in my opinion, is how we structure the project.

When it comes to managing large scale project which may consist of many modules, or even separate applications, a Nx Workspace based mono-repository is a good candidate for managing such a project. If you don't know what an Nx Workspace is, I encourage you to read my previous article where I introduce it along with monorepo fundamentals.

In this article series, I'll show you:

2 approaches for decomposing a project
How they can help you to better manage your project's codebase
What tools Nx Workspace provides us with that help us enforce boundaries within a project

Modules vs libraries

It is a well-known good practice, especially when working with a complex Web Application, to divide the functionality into separate, self-contained, and, when possible, reusable modules. This is a great principle and many modern CLIs (ie. Angular, Nest) provide us with tooling for creating such modules with ease, so we don't waste time creating additional module structure by hand.

Of course, we could take it a step further, and instead of just creating a separate module, create a whole separate library instead. This seems to be a bit of an overkill at first, but when we consider that Nx CLI provides us with just as easy a way of creating a library as we did for a module, it doesn't feel so daunting anymore. With that in mind, let's consider what the benefits of creating a separate library instead of just a module are:

libs may result in faster builds
- nx affected command will run the lint, test, build, or any other target only for the libraries that were affected by a given change
- with buildable libs and incremental builds, we can scale our repo even further
libs enable us to enforce stricter boundaries
code sharing and minimizing bundle size is easier with libs
- we can extract and publish reusable parts of our codebase
- with small and focused libraries we only import small pieces into the application (in the case of multi-app monorepo)

Decomposition strategies - horizontal

In this article, I want to focus on horizontal decomposition strategy, which is great not only for large, enterprise projects, but for smaller applications as well. Horizontal decomposition focuses on splitting the project into layers that are focused on a single technical functionality aspect of the module. A good example of libraries type in this case is:

application layer
feature layer
business logic layer
api/data access layer
presentational components layer

As you may see in this example layering concept, each of the library types has a specific responsibility that can be encapsulated. I have created an example application that demonstrates how the aforementioned decomposition can be applied into even a simple example app. You can find the source code on my repository. Please check out the post/nx-decomposition-p1 branch to get the code related to this post. This application allows a user to see a list of photos and like or dislike them. It is a very simple use case, but even here, we can distinguish few layers of code:

photo-fe - frontend application top layer
photo-feature-list - this is a feature layer. It collects data from data-access layer, and displays it using ui presentational components.
photo-data-access - this is a layer responsible for accessing and storing the data. This is where we include calls to the API and store the received data using NgRx store.
photo-ui - this library contains all the presentational components necessary to display the list of photos
photo-api-model, photo-model - those are libraries that contain data model structure used either in the API (it's shared by FE and BE applications), and the internal frontend model. API and internal models are the same now but this approach gives us the flexibility to, for example, stop API breaking changes from affecting the whole FE application. To achieve this we could just convert from API to internal model, and vice-versa.

This application decomposition allows for easier modifications of internal layer implementation. As long as we keep the interface intact, we can add additional levels of necessary logic, and not worry about affecting other layers. This way we can split responsibility between team members or whole teams.

Nx workspace comes with a great toolset for managing dependencies between the internal libraries. A great starting point to get a grasp of the repository structure is to visualize the repository structure, and its dependencies. The following command will show us all libraries within a monorepo and dependencies between those libraries:

nx dep-graph

It will open a dependency graph in a browser. From the left side menu, you can choose which projects you want to include in the visualization. After clicking Select all, you should see the following graph:

You can read more about dependecy graph here:

Enforce boundaries

As you may see in the dependency graph above, our application layer is accessing only certain other parts/libraries. As the project grows, we would probably like to make sure that the code still follows a given structure. I.e. we would not like UI presentational components to access any data access functionality of the application. Their only responsibility should be to display the provided data, and propagate user's interactions via output properties. This is where Nx tags comes in very handy. We can assign each library its own set of predefined tags, and then create boundaries base on those tags. For this example application, let's define the following set of tags:

type:application
type:feature
type:data-access
type:ui
type:model
type:api-model
type:be

Now, within the nx.json file, we can assign those tags to specific libraries to reflect its intent:

  "projects": {
    "photo-api-model": {
      "tags": [
        "type:api-model"
      ]
    },
    "photo-data-access": {
      "tags": [
        "type:data-access"
      ]
    },
    "photo-feature-list": {
      "tags": [
        "type:feature"
      ]
    },
    "photo-model": {
      "tags": [
        "type:model"
      ]
    },
    "photo-ui": {
      "tags": [
        "type:ui"
      ]
    },
    "photo-fe": {
      "tags": [
        "type:app"
      ]
    },
    "photo-api": {
      "tags": [
        "type:be"
      ]
    }
  }

Now that we have our tags defined, we can use either an ESLint or TSLint rule provided by Nrwl Nx to restrict access between libraries. Those rules are named @nrwl/nx/enforce-module-boundaries and nx-enforce-module-boundaries for ESLint and TSLint respectively. Let's define our allowed libraries anteriactions as follows:

type:application - can only access type:feature libraries
type:feature - can only access type:data-access, type:model, type:ui libraries
type:data-access - can only access type:api-model, type:model libraries
type:ui - can only access type:ui, type:model libraries
type:model - can not access other libraries
type:api-model - can not access other libraries
type:be - can only access type:api-model libraries

To enforce those constraints we can add each of the rules mentioned above to the @nrwl/nx/enforce-module-boundaries, or nx-enforce-module-boundaries configuration. Let's open the top level .eslintrc.json or .tslint.json files, and replace the default configuration with the following one:

"@nrwl/nx/enforce-module-boundaries": [
  "error",
  {
    "enforceBuildableLibDependency": true,
    "allow": [],
    "depConstraints": [
      {
        "sourceTag": "type:app",
        "onlyDependOnLibsWithTags": ["type:feature"]
      },
      {
        "sourceTag": "type:feature",
        "onlyDependOnLibsWithTags": ["type:data-access","type:model", "type:ui"]
      },
      {
        "sourceTag": "type:data-access",
        "onlyDependOnLibsWithTags": ["type:api-model", "type:model"]
      },
      {
        "sourceTag": "type:ui",
        "onlyDependOnLibsWithTags": ["type:ui", "type:model"]
      },
      {
        "sourceTag": "type:be",
        "onlyDependOnLibsWithTags": ["type:api-model"]
      }

    ]
  }
]

For type:model and type:api-model, we can either not include any configuration, or explicitly add configuration with an empty array of allowed tags:

{
  "sourceTag": "type:model",
  "onlyDependOnLibsWithTags": []
},
{
  "sourceTag": "type:api-model",
  "onlyDependOnLibsWithTags": []
}

Now, you can run the following command to verify that all constraints are met:

nx run-many --target=lint --all

You can set up the CI to run this check for all the PRs to the repository, and therefore, avoid including code that does not follow the architectural pattern that you decided for your project.

If any of the aforementioned constrains were violated, the linting process would produce an error like this

A project tagged with "type:data-access" can only depend on projects tagged with "type:api-model" or "type:model".

This gives a clear message on what the problem is, and tells the developer that they are trying to do something that should not be done.

You can read more about Nx tags & constraints in the documentation.

Conclusion

When designing a software solution that is expected to grow and be maintained for a long time, it is crucial to create an architecture that will support that goal. Composing an application out of well-defined and separated horizontal layers is a great tool that can be applied to a variety of projects - even the smaller ones. Nx comes with a built-in generic mechanism that allows system architects to impose their architectural decisions on a project and prevent unrestrained access between libraries. Additionally, with a help of Nx CLI, it is just as fast and easy to create new libraries as with creating a new module. So why not take advantage of it?

In case you have any questions, you can always tweet or DM me @ktrz. I'm always happy to help!

This Dot is a consultancy dedicated to guiding companies through their modernization and digital transformation journeys. Specializing in replatforming, modernizing, and launching new initiatives, we stand out by taking true ownership of your engineering projects.

We love helping teams with projects that have missed their deadlines or helping keep your strategic digital initiatives on course. Check out our case studies and our clients that trust us with their engineering.

About the author(s)

Chris Trześniewski
Software Engineer, ultra-marathoner. He is passionate about functional programming in JavaScript and loves working with RxJS. In his free time he likes to go jogging.
@ktrz__@ktrz

Exploring Angular Forms: A New Alternative with Signals

Exploring Angular Forms: A New Alternative with Signals In the world of Angular, forms are essential for user interaction, whether you're crafting a simple login page or a more complex user profile interface. Angular traditionally offers two primary approaches: template-driven forms and reactive forms. In my previous series on Angular Reactive Forms, I explored how to harness reactive forms' power to manage complex logic, create dynamic forms, and build custom form controls. A new tool for managing reactivity - signals - has been introduced in version 16 of Angular and has been the focus of Angular maintainers ever since, becoming stable with version 17. Signals allow you to handle state changes declaratively, offering an exciting alternative that combines the simplicity of template-driven forms with the robust reactivity of reactive forms. This article will examine how signals can add reactivity to both simple and complex forms in Angular. Recap: Angular Forms Approaches Before diving into the topic of enhancing template-driven forms with signals, let’s quickly recap Angular's traditional forms approaches: 1. Template-Driven Forms: Defined directly in the HTML template using directives like ngModel, these forms are easy to set up and are ideal for simple forms. However, they may not provide the fine-grained control required for more complex scenarios. Here's a minimal example of a template-driven form: ` ` 2. Reactive Forms: Managed programmatically in the component class using Angular's FormGroup, FormControl, and FormArray classes; reactive forms offer granular control over form state and validation. This approach is well-suited for complex forms, as my previous articles on Angular Reactive Forms discussed. And here's a minimal example of a reactive form: ` ` Introducing Signals as a New Way to Handle Form Reactivity With the release of Angular 16, signals have emerged as a new way to manage reactivity. Signals provide a declarative approach to state management, making your code more predictable and easier to understand. When applied to forms, signals can enhance the simplicity of template-driven forms while offering the reactivity and control typically associated with reactive forms. Let’s explore how signals can be used in both simple and complex form scenarios. Example 1: A Simple Template-Driven Form with Signals Consider a basic login form. Typically, this would be implemented using template-driven forms like this: ` ` This approach works well for simple forms, but by introducing signals, we can keep the simplicity while adding reactive capabilities: ` ` In this example, the form fields are defined as signals, allowing for reactive updates whenever the form state changes. The formValue signal provides a computed value that reflects the current state of the form. This approach offers a more declarative way to manage form state and reactivity, combining the simplicity of template-driven forms with the power of signals. You may be tempted to define the form directly as an object inside a signal. While such an approach may seem more concise, typing into the individual fields does not dispatch reactivity updates, which is usually a deal breaker. Here’s an example StackBlitz with a component suffering from such an issue: Therefore, if you'd like to react to changes in the form fields, it's better to define each field as a separate signal. By defining each form field as a separate signal, you ensure that changes to individual fields trigger reactivity updates correctly. Example 2: A Complex Form with Signals You may see little benefit in using signals for simple forms like the login form above, but they truly shine when handling more complex forms. Let's explore a more intricate scenario - a user profile form that includes fields like firstName, lastName, email, phoneNumbers, and address. The phoneNumbers field is dynamic, allowing users to add or remove phone numbers as needed. Here's how this form might be defined using signals: ` > Notice that the phoneNumbers field is defined as a signal of an array of signals. This structure allows us to track changes to individual phone numbers and update the form state reactively. The addPhoneNumber and removePhoneNumber methods update the phoneNumbers signal array, triggering reactivity updates in the form. ` > In the template, we use the phoneNumbers signal array to dynamically render the phone number input fields. The addPhoneNumber and removePhoneNumber methods allow users to reactively add or remove phone numbers, updating the form state. Notice the usage of the track function, which is necessary to ensure that the ngFor directive tracks changes to the phoneNumbers array correctly. Here's a StackBlitz demo of the complex form example for you to play around with: Validating Forms with Signals Validation is critical to any form, ensuring that user input meets the required criteria before submission. With signals, validation can be handled in a reactive and declarative manner. In the complex form example above, we've implemented a computed signal called formValid, which checks whether all fields meet specific validation criteria. The validation logic can easily be customized to accommodate different rules, such as checking for valid email formats or ensuring that all required fields are filled out. Using signals for validation allows you to create more maintainable and testable code, as the validation rules are clearly defined and react automatically to changes in form fields. It can even be abstracted into a separate utility to make it reusable across different forms. In the complex form example, the formValid signal ensures that all required fields are filled and validates the email and phone numbers format. This approach to validation is a bit simple and needs to be better connected to the actual form fields. While it will work for many use cases, in some cases, you might want to wait until explicit "signal forms" support is added to Angular. Tim Deschryver started implementing some abstractions around signal forms, including validation and wrote an article about it. Let's see if something like this will be added to Angular in the future. Why Use Signals in Angular Forms? The adoption of signals in Angular provides a powerful new way to manage form state and reactivity. Signals offer a flexible, declarative approach that can simplify complex form handling by combining the strengths of template-driven forms and reactive forms. Here are some key benefits of using signals in Angular forms: 1. Declarative State Management: Signals allow you to define form fields and computed values declaratively, making your code more predictable and easier to understand. 2. Reactivity: Signals provide reactive updates to form fields, ensuring that changes to the form state trigger reactivity updates automatically. 3. Granular Control: Signals allow you to define form fields at a granular level, enabling fine-grained control over form state and validation. 4. Dynamic Forms: Signals can be used to create dynamic forms with fields that can be added or removed dynamically, providing a flexible way to handle complex form scenarios. 5. Simplicity: Signals can offer a simpler, more concise way to manage form states than traditional reactive forms, making building and maintaining complex forms easier. Conclusion In my previous articles, we explored the powerful features of Angular reactive forms, from dynamic form construction to custom form controls. With the introduction of signals, Angular developers have a new tool that merges the simplicity of template-driven forms with the reactivity of reactive forms. While many use cases warrant Reactive Forms, signals provide a fresh, powerful alternative for managing form state in Angular applications requiring a more straightforward, declarative approach. As Angular continues to evolve, experimenting with these new features will help you build more maintainable, performant applications. Happy coding!...

Oct 4, 2024

5 mins

AngularJavaScript

How to build an AI assistant with OpenAI, Vercel AI SDK, and Ollama with Next.js

In today’s blog post, we’ll build an AI Assistant using three different AI models: Whisper and TTS from OpenAI and Llama 3.1 from Meta. While exploring AI, I wanted to try different things and create an AI assistant that works by voice. This curiosity led me to combine OpenAI’s Whisper and TTS models with Meta’s Llama 3.1 to build a voice-activated assistant. Here’s how these models will work together: * First, we’ll send our audio to the Whisper model, which will convert it from speech to text. * Next, we’ll pass that text to the Llama 3.1 model. Llama will understand the text and generate a response. * Finally, we’ll take Llama’s response and send it to the TTS model, turning the text back into speech. We’ll then stream that audio back to the client. Let’s dive in and start building this excellent AI Assistant! Getting started We will use different tools to build our assistant. To build our client side, we will use Next.js. However, you could choose whichever framework you prefer. To use our OpenAI models, we will use their TypeScript / JavaScript SDK. To use this API, we require the following environmental variable: OPENAI_API_KEY— To get this key, we need to log in to the OpenAI dashboard and find the API keys section. Here, we can generate a new key. Awesome. Now, to use our Llama 3.1 model, we will use Ollama and the Vercel AI SDK, utilizing a provider called ollama-ai-provider. Ollama will allow us to download our preferred model (we could even use a different one, like Phi) and run it locally. The Vercel SDK will facilitate its use in our Next.js project. To use Ollama, we just need to download it and choose our preferred model. For this blog post, we are going to select Llama 3.1. After installing Ollama, we can verify if it is working by opening our terminal and writing the following command: Notice that I wrote “llama3.1” because that’s my chosen model, but you should use the one you downloaded. Kicking things off It's time to kick things off by setting up our Next.js app. Let's start with this command: ` After running the command, you’ll see a few prompts to set the app's details. Let's go step by step: * Name your app. * Enable app router. The other steps are optional and entirely up to you. In my case, I also chose to use TypeScript and Tailwind CSS. Now that’s done, let’s go into our project and install the dependencies that we need to run our models: ` Building our client logic Now, our goal is to record our voice, send it to the backend, and then receive a voice response from it. To record our audio, we need to use client-side functions, which means we need to use client components. In our case, we don’t want to transform our whole page to use client capabilities and have the whole tree in the client bundle; instead, we would prefer to use Server components and import our client components to progressively enhance our application. So, let’s create a separate component that will handle the client-side logic. Inside our app folder, let's create a components folder, and here, we will be creating our component: ` Let’s go ahead and initialize our component. I went ahead and added a button with some styles in it: ` And then import it into our Page Server component: ` Now, if we run our app, we should see the following: Awesome! Now, our button doesn’t do anything, but our goal is to record our audio and send it to someplace; for that, let us create a hook that will contain our logic: ` We will use two APIs to record our voice: navigator and MediaRecorder. The navigator API will give us information about the user’s media devices like the user media audio, and the MediaRecorder will help us record the audio from it. This is how they’re going to play out together: ` Let’s explain this code step by step. First, we create two new states. The first one is for keeping track of when we are recording, and the second one stores the instance of our MediaRecorder. ` Then, we’ll create our first method, startRecording. Here, we are going to have the logic to start recording our audio. We first check if the user has media devices available thanks to the navigator API that gives us information about the browser environment of our user: If we don’t have media devices to record our audio, we just return. If they do, then let us create a stream using their audio media device. ` Finally, we go ahead and create an instance of a MediaRecorder to record this audio: ` Then we need a method to stop our recording, which will be our stopRecording. Here, we will just stop our recording in case a media recorder exists. ` We are recording our audio, but we are not storing it anywhere. Let’s add a new useEffect and ref to accomplish this. We would need a new ref, and this is where our chunks of audio data will be stored. ` In our useEffect we are going to do two main things: store those chunks in our ref, and when it stops, we are going to create a new Blob of type audio/mp3: ` It is time to wire this hook with our AudioRecorder component: ` Let’s go to the other side of the coin, the backend! Setting up our Server side We want to use our models on the server to keep things safe and run faster. Let’s create a new route and add a handler for it using route handlers from Next.js. In our App folder, let’s make an “Api” folder with the following route in it: We want to use our models on the server to keep things safe and run faster. Let’s create a new route and add a handler for it using route handlers from Next.js. In our App folder, let’s make an “Api” folder with the following route in it: ` Our route is called ‘chat’. In the route.ts file, we’ll set up our handler. Let’s start by setting up our OpenAI SDK. ` In this route, we’ll send the audio from the front end as a base64 string. Then, we’ll receive it and turn it into a Buffer object. ` It’s time to use our first model. We want to turn this audio into text and use OpenAI’s Whisper Speech-To-Text model. Whisper needs an audio file to create the text. Since we have a Buffer instead of a file, we’ll use their ‘toFile’ method to convert our audio Buffer into an audio file like this: ` Notice that we specified “mp3”. This is one of the many extensions that the Whisper model can use. You can see the full list of supported extensions here: https://platform.openai.com/docs/api-reference/audio/createTranscription#audio-createtranscription-file Now that our file is ready, let’s pass it to Whisper! Using our OpenAI instance, this is how we will invoke our model: ` That’s it! Now, we can move on to the next step: using Llama 3.1 to interpret this text and give us an answer. We’ll use two methods for this. First, we’ll use ‘ollama’ from the ‘ollama-ai-provider’ package, which lets us use this model with our locally running Ollama. Then, we’ll use ‘generateText’ from the Vercel AI SDK to generate the text. Side note: To make our Ollama run locally, we need to write the following command in the terminal: ` ` Finally, we have our last model: TTS from OpenAI. We want to reply to our user with audio, so this model will be really helpful. It will turn our text into speech: ` The TTS model will turn our response into an audio file. We want to stream this audio back to the user like this: ` And that’s all the whole backend code! Now, back to the frontend to finish wiring everything up. Putting It All Together In our useRecordVoice.tsx hook, let's create a new method that will call our API endpoint. This method will also take the response back and play to the user the audio that we are streaming from the backend. ` Great! Now that we’re getting our streamed response, we need to handle it and play the audio back to the user. We’ll use the AudioContext API for this. This API allows us to store the audio, decode it and play it to the user once it’s ready: ` And that's it! Now the user should hear the audio response on their device. To wrap things up, let's make our app a bit nicer by adding a little loading indicator: ` Conclusion In this blog post, we saw how combining multiple AI models can help us achieve our goals. We learned to run AI models like Llama 3.1 locally and use them in our Next.js app. We also discovered how to send audio to these models and stream back a response, playing the audio back to the user. This is just one of many ways you can use AI—the possibilities are endless. AI models are amazing tools that let us create things that were once hard to achieve with such quality. Thanks for reading; now, it’s your turn to build something amazing with AI! You can find the complete demo on GitHub: AI Assistant with Whisper TTS and Ollama using Next.js...

Sep 27, 2024

8 mins

AINextJSJavaScript

Introduction to Directives Composition API in Angular

In version 15, Angular introduced a new directives composition API that allows developers to compose existing directives into new more complex directives or components. This allows us to encapsulate behaviors into smaller directives and reuse them across the application more easily. In this article, we will explore the new API, and see how we can use it in our own components. All the examples from this article (and more) can be found on Stackblitz here. Starting point In the article, I will use two simple directives as an example * HighlightDirective - a directive borrowed from Angular's Getting started guide. This directive will change an element's background color whenever the element hovers. ` Fig. 1 * BorderDirective - a similar directive that will apply a border of a specified color to the element whenever it hovers ` Fig. 2 We can now easily apply our directives to any element we want ie. a paragraph: ` Fig. 3 However, if we wanted to apply both highlighting and border on hover we would need to add both directives explicitly: ` Fig. 4 With the new directives composition API, we can easily create another directive that composes behaviors of our 2 directives. Host Directives Angular 15 added a new property to the @Directive and @Component decorators. In this property, we can specify an array of different directives that we want our new component or directive to apply on a host element. We can do it as follows: ` As you can see in the above example, by just defining the hostDirectives property containing our highlight and border directives, we created a new directive that composes both behaviors into one directive. We can now achieve the same result as in Fig. 4 by using just a single directive: ` Fig. 5 Passing inputs and outputs Our newly composed directive works nicely already, but there is a problem. How do we pass properties to the directives that are defined in the hostDirectives array? They are not passed by default, but we can configure them to do so pretty easily by using an extended syntax: ` Fig. 6 This syntax takes exposes the "inner" directives\ color input from the HighlightAndBorderDirective`, and passes them down to both highlight and border directives. ` Fig. 7 This works, but we ended up with a border and highlight color both being blue. Luckily Angular's API allows us to easily redefine the properties' names using the : syntax. So let's remap out properties to highlightColor and borderColor so that the names don't collide with each other: ` Fig. 8 Now we can control both colors individually: ` Fig. 9 We could apply the same approach to mapping directive's outputs eg. ` Fig. 10 or ` Fig. 11 Adding host directives to a component Similarly to composing a directive out of other directives, we can apply the same approach to adding behavior to components using hostDirectives API. This way, we could, for example, create a more specialized component or just apply the behavior of the directive to a whole host element: ` Fig. 12 This component will render the paragraph, and apply both directives' behavior to the host element: ` Fig. 13 Just like we did for the directive, we can also expose and remap the directives inputs using the extended syntax. But if we would like to access and modify the directives inputs from within our component, we can also do that. This is where Angular's dependency injection comes in handy. We can inject the host directives via a constructor just like we would do for a service. After we have the directives instances available, we can modify them ie. in the ngOnInit lifecycle hook: ` Fig. 14 With this change, the code from Fig. 13 will use lightcoral as a background color and red as a border color. Performance Note While this API gives us a powerful tool-set for reusing behaviors across different components, it can impact the performance of our application if used excessively. For each instance of a given composed component Angular will create objects of the component class itself as well as an instance of each directive that it is composed of. If the component appears only a couple of times in the application. then it won't make a significant difference. However, if we create, for example, a composed checkbox component that appears hundreds of times in the app, this may have a noticeable performance impact. Please make sure you use this pattern with caution, and profile your application in order to find the right composition pattern for your application. Summary As I have shown in the above examples, the directives composition API can be a quite useful but easy-to-use tool for extracting behaviors into smaller directives and combining them into more complex behaviors. In case you have any questions, you can always tweet or DM me at @ktrz. I'm always happy to help!...

Jan 4, 2023

4 mins

TypeScriptDirectiveAngular

Understanding the Difference Between `:focus` and `:focus-visible` in CSS

Understanding the Difference Between :focus and :focus-visible in CSS I have learned my fair share about the importance of keyboard accessibility, so I know that visual indication of the focused element is very important. But the well-known :focus pseudo-class is not always the best fit for this job. That's where :focus-visible comes in. Let's look at the differences between these two pseudo-classes and explore the best practices for using them effectively. What is the :focus Pseudo-Class? The :focus pseudo-class is a CSS selector that applies styles to any element that receives focus, regardless of how that focus was triggered. This includes focus events from keyboard navigation, mouse clicks, and touch interactions. Example Usage of :focus ` In this example, the button will display a blue outline whenever it is focused, whether the user clicks on it with a mouse, taps it on a touchscreen, or navigates to it using the keyboard. What is the :focus-visible Pseudo-Class? The :focus-visible pseudo-class is more specialized. It only applies styles to an element when the browser determines that the focus should be visible. This typically occurs when the user navigates via the keyboard or assistive technologies rather than through mouse or touch input. Example Usage of :focus-visible ` Here, the button will only show a blue outline when focused through keyboard navigation or another input method that usually requires visible focus indicators. Key Differences Between :focus and :focus-visible :focus - Behavior: Applies to any element that receives focus, regardless of the input method. - Use Cases: Ensures that all interactions with the element are visually indicated, whether by mouse, keyboard, or touch. :focus-visible - Behavior: Applies styles only when the focus should be visible, such as using a keyboard or assistive technology. - Use Cases: Ideal for scenarios where you want to provide focus indicators only to keyboard and assistive technology users while avoiding unnecessary outlines for mouse and touch users, typically required by design. Accessibility Implications :focus - Pros: - Guarantees that all users can see when an element is focused, which is critical for accessibility. - Cons: - Can lead to a suboptimal experience for mouse users, as focus styles may appear unnecessarily during mouse interactions. :focus-visible - Pros: - Enhances user experience by showing focus indicators only when necessary, thus keeping the interface clean for mouse and touch users. - Tailors the experience for keyboard and assistive technology users, providing them with clear visual cues. - Cons: - Additional considerations may be required to ensure that focus indicators are not accidentally omitted, especially in older browsers that do not support :focus-visible. - There may be cases where you want to show focus indicators for all users, regardless of input method. Best Practices for Using :focus and :focus-visible To achieve the best accessibility and user experience, combining both :focus and :focus-visible in your CSS is often a good idea. Combining :focus and :focus-visible ` Here is a Stackblitz example of what such styling could look like for you to try out and play with. Additional Tips - Test with Keyboard and Assistive Technology: Ensure that your web application is navigable using a keyboard (Tab, Shift + Tab, etc.) and that focus indicators are visible for those who rely on them. It's never a bad idea to include accessibility testing in your e2e testing suite. - Provide Clear Focus Indicators: Make sure that focus indicators are prominent and easy to see. A subtle or hard-to-spot focus indicator can severely impact accessibility for users who rely on keyboard navigation. Conclusion The :focus-visible pseudo-class offers a more refined way to manage focus indicators, improving accessibility and user experience, particularly for keyboard and assistive technology users. By understanding the differences between :focus and :focus-visible, and applying best practices in your CSS, you can create more accessible and user-friendly web applications. Remember, accessibility should never be an afterthought. By thoughtfully applying focus styles, you ensure that all users, regardless of how they interact with your site, can easily navigate and interact....

Nov 8, 2024

3 mins

AccessibilityCSSPlaywright

Let's innovate together!

We're ready to be your trusted technical partners in your digital innovation journey.

Whether it's modernization or custom software solutions, our team of experts can guide you through best practices and how to build scalable, performant software that lasts.

Decomposing a project using Nx - Part 1

Decomposing a project using Nx - Part 1

Modules vs libraries

Decomposition strategies - horizontal

Enforce boundaries

Conclusion

Chris Trześniewski

You might also like

Exploring Angular Forms: A New Alternative with Signals

How to build an AI assistant with OpenAI, Vercel AI SDK, and Ollama with Next.js

Introduction to Directives Composition API in Angular

Understanding the Difference Between `:focus` and `:focus-visible` in CSS

Let's innovate together!

You might also like

Exploring Angular Forms: A New Alternative with Signals

How to build an AI assistant with OpenAI, Vercel AI SDK, and Ollama with Next.js

Introduction to Directives Composition API in Angular

Understanding the Difference Between `:focus` and `:focus-visible` in CSS