Javascript

Deploying Next.js Applications to Fly.io

Published Feb 21, 2024

Updated Feb 21, 2024

7 min read

Fly.io has gained significant popularity in the developer community recently, particularly after being endorsed by Kent C. Dodds for hosting his Epic Web project. It's a go-to choice for hobby projects, thanks to its starting plans that are effectively free, making it highly accessible for individual developers. While Next.js applications are often deployed to Vercel, Fly.io has emerged as a perfectly viable alternative, offering robust hosting solutions and global deployment capabilities. In this blog post, we'll give an overview of how to install a Next.js app to Fly.io, mentioning any gotchas you should be aware of along the way.

The Project

Our project is a simple Next.js project using the latest version of Next.js at the time of the writing (14). It uses the app directory and integrates with Spotify to get a list of episodes for our podcast, Modern Web. The bulk of the logic is in the page.tsx file, shown below, which represents the front page that is server-rendered after retrieving a list of episodes from Spotify.

// app/page.tsx
import { getEpisodes } from '@/spotify/spotify';

export default async function Home() {
  const modernWebShowId = '5FGA58foRFkJ6IgJbCFYgm';
  const show = await getEpisodes(modernWebShowId);

  return (
    <main className="flex min-h-screen flex-col items-center justify-between p-24">
      <div className="mb-32 grid text-center lg:max-w-5xl lg:w-full lg:mb-0 lg:grid-cols-4 lg:text-left">
        {show.episodes.items.map(
          (episode) => <a
            key={episode.id}
            href={episode.external_urls.spotify}
            className="group rounded-lg border border-transparent px-5 py-4 transition-colors hover:border-gray-300 hover:bg-gray-100 hover:dark:border-neutral-700 hover:dark:bg-neutral-800/30"
            target="_blank"
            rel="noopener noreferrer"
          >
            <h2 className={`mb-3 text-2xl font-semibold`}>
              {episode.name}{' '}
              <span
                className="inline-block transition-transform group-hover:translate-x-1 motion-reduce:transform-none">
              -&gt;
            </span>
            </h2>
            <p className={`m-0 max-w-[30ch] text-sm opacity-50`}>
              {episode.description}
            </p>
          </a>)}
      </div>
    </main>
  )
}

The getEpisodes is a custom function that is a wrapper around the Spotify API. It uses the Spotify client ID and secret (provided through the SPOTIFY_CLIENT_ID and SPOTIFY_CLIENT_SECRET environment variables, respectively) to get an access token from Spotify and invoke a REST endpoint to get a list of episodes for the given show ID. As can be seen from the above code, the Home is an async, server-rendered component.

Scaffolding of Fly.io Configuration

To get started with Fly.io and deploy a new project using flyctl, you need to go through a few simple steps: installing the flyctl CLI, logging into Fly.io, and using the flyctl launch command.

Installing the CLI

Installing flyctl is different depending on the operating system you use:

If you're on Windows, the easiest way to install flyctl is by using scoop, a command-line installer. First, install scoop if you haven’t already, then run scoop install flyctl in your command prompt or PowerShell.
For macOS users, you can use Homebrew, a popular package manager. Simply open your terminal and run brew install superfly/tap/flyctl.
Linux users can install flyctl by running the following script in the terminal: curl -L https://fly.io/install.sh | sh. This will download and install the latest version.

Logging In

After installing flyctl, the next step is to log in to your Fly.io account. Open your terminal or command prompt and enter flyctl auth login. This command will open a web browser prompting you to log in to Fly.io. If you don’t have an account, you can create one at this step. Once you're logged in through the browser, the CLI will automatically be authenticated.

Scaffolding the Fly.io Configuration

The next step is to use fly launch to add all the necessary files for deployment, such as a Dockerfile and a fly.toml file, which is the main Fly.io configuration file. This command initiates a few actions:

It detects your application type and proposes a configuration.
It sets up your application on Fly.io, including provisioning a new app name if you don’t specify one.
It allows you to select a region to deploy to. There are really many to choose from, so you can get really picky here.

Once the process completes, flyctl will be ready for deploying the application.

In our case, the process went like this:

➜ flyctl launch
Creating app in /Users/dario/Projects/blog-demos/thisdot-nextjs
Scanning source code
Detected a Next.js app
? Choose an app name (leave blank to generate one): thisdot-nextjs-blog
automatically selected personal organization: This Dot
Some regions require a paid plan (bom, fra, maa).
See https://fly.io/plans to set up a plan.

? Choose a region for deployment: Atlanta, Georgia (US) (atl)
App will use 'atl' region as primary

Created app 'thisdot-nextjs-blog' in organization 'personal'
Admin URL: https://fly.io/apps/thisdot-nextjs-blog
Hostname: thisdot-nextjs-blog.fly.dev
installing: npm install @flydotio/dockerfile@latest --save-dev

added 20 packages, and audited 352 packages in 5s

119 packages are looking for funding
  run `npm fund` for details

found 0 vulnerabilities
     create  Dockerfile
Wrote config file fly.toml
Validating /Users/dario/Projects/blog-demos/thisdot-nextjs/fly.toml
Platform: machines
✓ Configuration is valid

If you need custom packages installed, or have problems with your deployment
build, you may need to edit the Dockerfile for app-specific changes. If you
need help, please post on https://community.fly.io.

Now: run 'fly deploy' to deploy your Next.js app.

Deploying

Now, if this was a simpler Next.js app without any environment variables, running flyctl deploy would build the Docker image in a specialized "builder" app container on Fly.io and deploy that image to the app container running the app. However, in our case, executing flyctl deploy will fail:

#0 1.486    Creating an optimized production build ...
#0 9.227  ✓ Compiled successfully
#0 9.227    Linting and checking validity of types ...
#0 12.17    Collecting page data ...
#0 13.06    Generating static pages (0/5) ...
#0 13.26 Error: Missing client ID or secret
#0 13.26     at getEpisodes (/app/.next/server/app/page.js:1:2140)
#0 13.26     at Home (/app/.next/server/app/page.js:1:2884)

This is because our page is statically rendered, and the Next.js build process attempts to run Home, our server-rendered component to cache its output. Before we can do this, we need to add our environment variables so that Fly.io is aware of them, but this is somewhat a tricky subject, so let's explain why in the following chapter.

Handling of Secrets

Most complex web apps will need some secrets injected into the app via environment variables. Environment variables are a good way to inject sensitive information, such as API secret keys, to your web app without storing them in the repository, the file system, or any other unprotected place.

Unlike other providers such as the previously mentioned Vercel, Fly.io distinguishes built-time and run-time secrets, which are then injected as environment variables. Build-time secrets are those secrets that your app requires to build itself, while run-time secrets are needed while the app is running.

In our case, due to the fact that Next.js will attempt to cache our static pages upfront, the Spotify client ID and client secret are needed during both build-time and run-time (after the cache expires).

Build-Time Secrets

Our Next.js app is built while building the Docker image, therefore build-time secrets should be passed to the Docker context. The recommended, Docker-way of doing this, is through Docker's build-time secrets, which are added through a special --mount=type=secret flag to the RUN command that builds the site. This is a relatively newer feature that allows you to securely pass secrets needed during the build process without including them in the final image or even as an intermediate layer.

This means that, instead of having the following build command in our Dockerfile:

RUN npm run build

we will have:

RUN --mount=type=secret,id=SPOTIFY_CLIENT_ID \
    --mount=type=secret,id=SPOTIFY_CLIENT_SECRET \
    SPOTIFY_CLIENT_ID="$(cat /run/secrets/SPOTIFY_CLIENT_ID)" \
    SPOTIFY_CLIENT_SECRET="$(cat /run/secrets/SPOTIFY_CLIENT_SECRET)" \
    npm run build

Now, you can either modify the Dockerfile manually and add this, or you can use a helpful little utility called dockerfile:

npx dockerfile --mount-secret=SPOTIFY_CLIENT_ID --mount-secret=SPOTIFY_CLIENT_SECRET

If we were using docker build to build our image, we would pass the secret values like so:

docker build --secret id=SPOTIFY_CLIENT_ID,src=someClientId --secret id=SPOTIFY_CLIENT_SECRET,src=someClientSecret

However, in our case we use fly deploy, which is just a wrapper around docker build. To pass the secrets, we would use the following command:

fly deploy --build-secret SPOTIFY_CLIENT_ID=someClientId --build-secret SPOTIFY_CLIENT_SECRET=someClientSecret

And now, the app builds and deploys successfully in a few minutes.

To summarize, if you have any secrets which are necessary at build-time, they will need to be provided to the fly deploy command. This means that if you have a CI/CD pipeline, you will need to make sure that these secrets are available to your CI/CD platform. In the case of GitHub actions, they would need to be stored in your repository's secrets.

Run-Time Secrets

Run-time secrets are handled in a different way - you need to provide them to Fly.io via the fly secrets set command:

fly secrets set SPOTIFY_CLIENT_ID=someClientId SPOTIFY_CLIENT_SECRET=someClientSecret

Now, you might be wondering why fly deploy cannot use these secrets if they are already stored in Fly.io. The architecture of Fly.io is set up in such a way that reading these secrets through the API, once they are set, is not possible. Secrets are stored in an encrypted vault. When you set a secret using fly secrets set, it sends the secret value through the Fly.io API, which writes it to the vault for your specific Fly.io app. The API servers can only encrypt; they cannot decrypt secret values. Therefore, the fly deploy process, which is, if you remember, just a wrapper around docker build, cannot access the decrypted secret values.

Other Things to Consider

Beware of .env Files

In Next.js, you can use .env as well as .env.local for storing environment variable values for local development. However, keep in mind that only .env.local files are ignored by the Docker build process via the .dockerignore file generated by Fly.io. This means that if you happen to be using an .env file, this file could be bundled into your Docker image, which is potentially a security risk if it contains sensitive information. To prevent this from happening, be sure to add .env to your .dockerignore file as well.

Not Enough Memory?

For larger Next.js sites, you might run into situations where the memory of your instance is simply not enough to run the app, especially if you are on the hobby plan.

If that happens, you have two options.

The first one does not incur any additional costs, and it involves increasing the swap size. This is not ideal, as more disk operation is involved, making the entire process slower, but it is good enough if you don't have any other options. To set swap size to something like 512 MB, you need to add the following line to the fly.toml file near the top:

swap_size_mb = 512

The second one is increasing memory size of your instance. This does incur additional cost, however. If you decide to use this option, the command to use is:

fly scale memory [memoryInMb]

For example, to increase RAM memory to 1024 MB, you would use the command:

fly scale memory 1024

After making the changes, you can try redeploying and seeing if the process is still crashing due to lack of memory.

Conclusion

In conclusion, deploying Next.js applications to Fly.io offers a flexible and robust solution for developers looking for alternatives to more commonly used platforms like Vercel. We hope this blog post has provided you with some useful insights on the things to consider when doing so. Be sure to also check out our Next starter templates on starter.dev if you'd like to integrate a few other frameworks into your Next.js project.

The entire source code for this project is available on Stackblitz.

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)

Dario Djuric
Dario is a full-stack engineer who has spent most of his career doing enterprise Java projects. He has always had a hidden passion for frontend, though -- and he is now able to pursue that passion at This Dot. He spends most of his free time with his two sons, but occasionally, he manages to squeeze in some casual sports activities such as jogging, cycling, and soccer.
@dario_djuric @dariodjuric

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

Making Seamless Page Transitions with the View Transitions API

Make Seamless Page Transitions using the View Transitions API Traditionally web applications have always had a less polished experience, both functionally and visually speaking, compared to native applications on mobile and other platforms. This has been improving over the years with the introduction of new web APIs and standards. One such new API that bridges that gap is the View Transitions API. What is the View Transitions API? The View Transitions API is a new browser feature that allows developers to more easily create animated transitions between pages and views, similar to how mobile apps have transitions when navigating between pages. Adding view transitions to your application can reduce the cognitive load on your users and make the experience feel less inconsistent. One great thing about view transitions is that they can be used by both SPAs (single-page applications) and MPAs (multi-page applications) alike! Using the View Transitions API for SPAs Let’s see how we can use view transitions ourselves. We will use examples adapted from demos created by the wonderful Jake Archibald and Bramus. There are some alterations to a few of the examples to make them work with the newest version of the View Transitions API, but otherwise, they’re mostly the same. The process of getting view transitions working for SPAs is as simple as calling document.startViewTransition right before starting navigation and replacing your page’s content immediately after that. You need to make this call in your router or in an event listener that triggers your page navigation. The following is an example of how you can hook into page navigation in a vanilla JavaScript application. This example uses the Navigation API if it’s available. As of the time of writing this article only Chromium-based browsers support the Navigation API, but there is a fallback to using a click listener on link elements so that this works with Firefox and Safari. ` If you’re using a framework, then there’s probably a better way to do this specific to that framework than by using this contrived example. For example, if you are implementing usage of this API with a Next application, you may opt for something like next-view-transitions. For this article, we’re going to focus on the way this is done in vanilla JavaScript so that the fundamentals of using the API are understood. Knowing how it’s implemented in vanilla JavaScript should give you enough understanding to implement this in the framework of your choice if there isn’t already a library that does it for you. Now we have a way to know when a navigation is being requested. Using the above utility function we can start the animation and replace our content as follows: ` getMyPageContentSomehow can be any function that returns HTML. How that is implemented will depend on your application and framework of choice. If you want to run these examples on your machine, the demo repository has instructions in its README. In essence, it’s as simple as hosting an HTTP server at the root of the project and visiting localhost. No fancy build system is required! By default, a fade is done that only lasts for a moment. The animation that happens can be configured using CSS with the ::view-transition-group pseudo-element on the element you want to animate, which in our case is the entire page, and we pass in root. ` Given that the animation properties are used, you have much control over what kind of animation to do. You can change the duration and the interpolation, add keyframes and gradients, and even add image masks. You can learn more about the animation properties in CSS on MDN. Using the View Transitions API for MPAs As mentioned, view transitions also work for MPAs with no client-side routing. Setting up view transitions for MPAs is much easier than for SPAs. All you have to do is include the CSS rule on all pages where you want the animations. ` Of course, like how it works with SPAs, you can customize the animations to your heart’s content using the view transition pseudo-elements. Conclusion I hope this article helped you understand the fundamentals of using the View Transitions API. Now that most browsers support it and it’s not too difficult to add it to existing applications, it is a great time to learn how to use it. You can view a full list of examples here (made by Jake Archibald and Bramus) if you want to see how advanced the animations can get. I like the Star Wars ones especially. The README explains how to get all the examples up and running (a total of 43!)....

Aug 2, 2024

4 mins

JavaScript

Communication Between Client Components in Next.js

Communication Between Client Components in Next.js In recent years, Next.js has become one of the most popular React frameworks for building server-rendered applications. With the introduction of the App Router in Next.js 13, the framework has taken a major leap forward by embracing a new approach to building web applications: the concept of server components and client components. This separation of concerns allows developers to strategically decide which parts of their application should be rendered on the server and which are then hydrated in the browser for interactivity. The Challenge: Communicating Between Client Components While server components offer numerous benefits, they also introduce a new challenge: how can client components within different boundaries communicate with each other? For instance, let's consider a scenario where you have a button (a client component) and a separate client component that displays the number of times the button has been clicked. In a regular React application, this would typically be accomplished by lifting the state to a common ancestor component, allowing the button to update the state, which is then passed down to the counter display component. However, the traditional approach may not be as straightforward in a Next.js application that heavily relies on server components, with client components scattered across the page. This blog post will explore three ways to facilitate communication between client components in Next.js, depending on where you want to store the state. Lifting State to a Common Client Component One approach to communicating between client components is to lift the state to a common client component. This could be a React context provider, a state management system like Zustand, or any other solution that allows you to share state across components. The key aspect is that this wrapper component should be higher up in the tree (perhaps even in the layout) and accept server components as children. Next.js allows you to interleave client and server components as much as you want, as long as server components are passed to client components as props or as component children. Here's how this approach might look in practice. First of all, we'd create a wrapper client component that holds the state: ` This wrapper component can be included in the layout: ` Any server components can be rendered within the layout, potentially nesting them several levels deep. Finally, we'd create two client components, one for the button and one for the counter display: ` ` The entire client and server component tree is rendered on the server, and the client components are then hydrated in the browser and initialized. From then on, the communication between the client components works just like in any regular React application. Check out the page using this pattern in the embedded Stackblitz window below: Using Query Params for State Management Another approach is to use query params instead of a wrapper client component and store the state in the URL. In this scenario, you have two client components: the button and the counter display. The counter value (the state) is stored in a query param, such as counterValue. The client components can read the current counter value using the useSearchParams hook. Once read, the useRouter hook can update the query param, effectively updating the counter value. However, there's one gotcha to this approach. If a route is statically rendered, calling useSearchParams will cause the client component tree up to the closest Suspense boundary to be client-side rendered. Next.js recommends wrapping the client component that uses useSearchParams in a boundary. Here's an example of how this approach might look. The button reads the current counter value and updates it on click by using the router's replace function: ` The counter display component is relatively simple, only reading the counter value: ` And here is the page that is a server component, hosting both of the above client components: ` Feel free to check out the above page in the embedded Stackblitz below: Storing State on the Server The third approach is to store the state on the server. In this case, the counter display component accepts the counter value as a prop, where the counter value is passed by a parent server component that reads the counter value from the database. The button component, when clicked, calls a server action that updates the counter value and calls revalidatePath() so that the counter value is refreshed, and consequently, the counter display component is re-rendered. It's worth noting that in this approach, unless you need some interactivity in the counter display component, it doesn't need to be a client component – it can be purely server-rendered. However, if both components need to be client components, here's an example of how this approach might look. First, we'll implement a server action that updates the counter value. We won't get into the mechanics of updating it, which in a real app would require a call to the database or an external API - so we're only commenting that part. After that, we revalidate the path so that the Next.js caches are purged, and the counter value is retrieved again in server components that read it. ` The button is a simple client component that calls the above server action when clicked. ` The counter display component reads the counter value from the parent: ` While the parent is a server component that reads the counter value from the database or an external API. ` This pattern can be seen in the embedded Stackblitz below: Conclusion Next.js is a powerful framework that offers various choices for implementing communication patterns between client components. Whether you lift state to a common client component, use query params for state management, or store the state on the server, Next.js provides all the tools you need to add a communication path between two separate client component boundaries. We hope this blog post has been useful in demonstrating how to facilitate communication between client components in Next.js. Check out other Next.js blog posts we've written for more insights and best practices. You can also view the entire codebase for the above snippets on StackBlitz....

Jun 21, 2024

5 mins

NextJSReact

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

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Dario Djuric

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