ReactJS

Why You Should Use React Query or SWR

Published Sep 25, 2020

Updated Feb 14, 2023

6 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This article was written over 18 months ago and may contain information that is out of date.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.

Why You Should Use React Query or SWR for External Data

Most of us out here in React-land are out building our apps with the shiny new Hooks API and slinging requests to external APIs like nobody's business. Those of us new to hooks may have started creating hooks that look like this simplified example:

export const useSearch = () => {
    const [query, setQuery] = useState();
    const [results, setResults] = useState();

    useEffect(() => {
        if (!query)
            return;

        api.search(query)
            .then(results => setResults(results));
    }, [query]);

    return {
        query,
        setQuery,
        results
    }
}

The Problem

However, each time a hook is called, that instance is unique to the component it was called in, so there's a few issues we'll run into:

Updating the above query in one instance won't update it for the other instances.
If we have three components using a hook that makes an API request, we will get at least one request per component.
If we have multiple requests in the air, and we try to store them in a global store or have the hook hold the state, we will end up with state out of sync or subsequent requests overwriting each other.

One way to solve this is to leave the request out of hooks and only call it on mount in a component you can guarantee will be singular (aka a singleton component, like a page/route perhaps). Depending on how that data is used, this can sometimes be very tricky to do.

Potential Solutions

So what can we do? There are a few options:

Make sure the data returned from the API goes in context or some kind of global state management, accepting the multiple requests (potentially overexerting our server's API)
Doing the above + use a library like react-singleton-hook, ensure there's only a single component with the useEffect doing the API call, or similar to prevent multiple requests.
Implement some kind of way to cache the data (while still being able to invalidate that as necessary) so that we pull from the cache first
Use React Query or SWR

The Real Solution

The real solution here is to use option 4. Both libraries have done really sophisticated work to solve these problems to prevent you from having to do it yourself. Caching is extremely tricky to get right and can have pretty dire consequences if done poorly, leading to your app partially or completely breaking.

Feature Comparison

React Query's creator did a great job building out a feature table comparison for React Query, SWR, and Apollo for you to see what features are available. One big feature that I'd like to call out that React Query has over SWR is it's own set of dev tools which are really helpful for debugging misbehaving queries.

Conclusion

Over my time as a developer, I've tried to solve these problems myself, and had I had a library like React Query or SWR, I would have saved a ton of time. These problems can be really tricky to solve and the solutions can inject subtle bugs into your app that are difficult or time-consuming to debug. Luckily, we have open source and these people were generous to offer their own robust solutions for us to use.

If you'd like to see more about the problems these libraries solve and the efforts needed to solve them, Tanner Linsley did a great walkthrough of what he was experiencing and how he solved it. You can watch that walkthrough here:

Overall I think these libraries are great additions to the ecosystem and will help us all write better software. I'd like to see other frameworks come out with similar libraries, because the concepts here are pretty universal. I hope you found this helpful and let us know any unique strategies you have when using these libraries!

PS. What about GraphQL? 😂

Well, a lot of the GraphQL libraries out there actually built in a lot of these concepts from the get go, so if you're using something like Apollo or Urql, you probably are already getting these benefits. However, both libraries are compatible with anything that returns a promise, so if your particular favorite GQL library doesn't have these features, try putting React Query or SWR in front of it. 😁

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About the author(s)

Hunter Miller
@hmillerdev @arthyn

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 Zod for Data Validation

As web developers, we're often working with data from external sources like APIs we don't control or user inputs submitted to our backends. We can't always rely on this data to take the form we expect, and we can encounter unexpected errors when it deviates from expectations. But with the Zod library, we can define what our data ought to look like and parse the incoming data against those defined schemas. This lets us work with that data confidently, or to quickly throw an error when it isn't correct. Why use Zod? TypeScript is great for letting us define the shape of our data in our code. It helps us write more correct code the first time around by warning us if we are doing something we shouldn't. But TypeScript can't do everything for us. For example, we can define a variable as a string or a number, but we can't say "a string that starts with user_id_ and is 20 characters long" or "an integer between 1 and 5". There are limits to how much TypeScript can narrow down our data for us. Also, TypeScript is a tool for us developers. When we compile our code, our types are not available to the vanilla JavaScript. JavaScript can't validate that the data we actually use in our code matches what we thought we'd get when we wrote our TypeScript types unless you're willing to manually write code to perform those checks. This is where we can reach for a tool like Zod. With Zod, we can write data schemas. These schemas, in the simplest scenarios, look very much like TypeScript types. But we can do more with Zod than we can with TypeScript alone. Zod schemas let us create additional rules for data parsing and validation. A 20-character string that starts with user_id_? It's z.string().startsWith('user_id_').length(20). An integer between 1 and 5 inclusive? It's z.number().int().gte(1).lte(5). Zod's primitives give us many extra functions to be more specific about *exactly* what data we expect. Unlike TypeScript, Zod schemas aren't removed on compilation to JavaScript—we still have access to them! If our app receives some data, we can verify that it matches the expected shape by passing it to your Zod schema's parse function. You'll either get back your data in exactly the shape you defined in your schema, or Zod will give you an error to tell you what was wrong. Zod schemas aren't a replacement for TypeScript; rather, they are an excellent complement. Once we've defined our Zod schema, it's simple to derive a TypeScript type from it and to use that type as we normally would. But when we really need to be sure our data conforms to the schema, we can always parse the data with our schema for that extra confidence. Defining Data Schemas Zod schemas are the variables that define our expectations for the shape of our data, validate those expectations, and transform the data if necessary to match our desired shape. It's easy to start with simple schemas, and to add complexity as required. Zod provides different functions that represent data structures and related validation options, which can be combined to create larger schemas. In many cases, you'll probably be building a schema for a data object with properties of some primitive type. For example, here's a schema that would validate a JavaScript object representing an order for a pizza: ` Zod provides a number of primitives for defining schemas that line up with JavaScript primitives: string, number, bigint, boolean, date, symbol, undefined, and null. It also includes primitives void, any, unknown, and never for additional typing information. In addition to basic primitives, Zod can define object, array, and other native data structure schemas, as well as schemas for data structures not natively part of JavaScript like tuple and enum. The documentation contains considerable detail on the available data structures and how to use them. Parsing and Validating Data with Schemas With Zod schemas, you're not only telling your program what data should look like; you're also creating the tools to easily verify that the incoming data matches the schema definitions. This is where Zod really shines, as it greatly simplifies the process of validating data like user inputs or third party API responses. Let's say you're writing a website form to register new users. At a minimum, you'll need to make sure the new user's email address is a valid email address. For a password, we'll ask for something at least 8 characters long and including one letter, one number, and one special character. (Yes, this is not really the best way to write strong passwords; but for the sake of showing off how Zod works, we're going with it.) We'll also ask the user to confirm their password by typing it twice. First, let's create a Zod schema to model these inputs: ` So far, this schema is pretty basic. It's only making sure that whatever the user types as an email is an email, and it's checking that the password is at least 8 characters long. But it is *not* checking if password and confirmPassword match, nor checking for the complexity requirements. Let's enhance our schema to fix that! ` By adding refine with a custom validation function, we have been able to verify that the passwords match. If they don't, parsing will give us an error to let us know that the data was invalid. We can also chain refine functions to add checks for our password complexity rules: ` Here we've chained multiple refine functions. You could alternatively use superRefine, which gives you even more fine grained control. Now that we've built out our schema and added refinements for extra validation, we can parse some user inputs. Let's see two test cases: one that's bound to fail, and one that will succeed. ` There are two main ways we can use our schema to validate our data: parse and safeParse. The main difference is that parse will throw an error if validation fails, while safeParse will return an object with a success property of either true or false, and either a data property with your parsed data or an error property with the details of a ZodError explaining why the parsing failed. In the case of our example data, userInput2 will parse just fine and return the data for you to use. But userInput1 will create a ZodError listing all of the ways it has failed validation. ` ` We can use these error messages to communicate to the user how they need to fix their form inputs if validation fails. Each error in the list describes the validation failure and gives us a human readable message to go with it. You'll notice that the validation errors for checking for a valid email and for checking password length have a lot of details, but we've got three items at the end of the error list that don't really tell us anything useful: just a custom error of Invalid input. The first is from our refine checking if the passwords match, and the second two are from our refine functions checking for password complexity (numbers and special characters). Let's modify our refine functions so that these errors are useful! We'll add our own error parameters to customize the message we get back and the path to the data that failed validation. ` Now, our error messages from failures in refine are informative! You can figure out which form fields aren't validating from the path, and then display the messages next to form fields to let the user know how to remedy the error. ` By giving our refine checks a custom path and message, we can make better use of the returned errors. In this case, we can highlight specific problem form fields for the user and give them the message about what is wrong. Integrating with TypeScript Integrating Zod with TypeScript is very easy. Using z.infer<typeof YourSchema> will allow you to avoid writing extra TypeScript types that merely reflect the intent of your Zod schemas. You can create a type from any Zod schema like so: ` Using a TypeScript type derived from a Zod schema does *not* give you any extra level of data validation at the type level beyond what TypeScript is capable of. If you create a type from z.string.min(3).max(20), the TypeScript type will still just be string. And when compiled to JavaScript, even that will be gone! That's why you still need to use parse/safeParse on incoming data to validate it before proceeding as if it really does match your requirements. A common pattern with inferring types from Zod schemas is to use the same name for both. Because the schema is a variable, there's no name conflict if the type uses the same name. However, I find that this can lead to confusing situations when trying to import one or the other—my personal preference is to name the Zod schema with Schema at the end to make it clear which is which. Conclusion Zod is an excellent tool for easily and confidently asserting that the data you're working with is exactly the sort of data you were expecting. It gives us the ability to assert at runtime that we've got what we wanted, and allows us to then craft strategies to handle what happens if that data is wrong. Combined with the ability to infer TypeScript types from Zod schemas, it lets us write and run more reliable code with greater confidence....

Nov 15, 2024

7 mins

JavaScriptZod

What Can You Do with Tailwind 2?

What Can You Do with Tailwind 2? Tailwind CSS finally announced it's new 2.0 version on November 18th, 2020. If you've been around the industry lately, you've probably heard of Tailwind CSS, because they seem to be everywhere! But maybe you haven't, and in that case, it's time for a short introduction. If you have, skip to 2.0 Features Intro to Tailwind CSS Something that started out as some handcrafted CSS used personally by Adam Wathan became an extremely popular, productivity-boosting CSS framework used by millions over the last three years. The core mindset behind Tailwind is that CSS should be composed of small single-purpose utility classes which pull from a standard set of values. At first you may think, "This seems like inline styles, why would I want to bloat my markup and aren't those bad anyway?", and while your statement about inline styles is correct, Tailwind's utility classes aren't that at all. Here's why: 1. By sticking with only classes, styles all stay at the same specificity. 2. By using classes instead of inline styles, we encourage reuse and ensure that we're sticking to a standard set of values. 3. While our markup may be larger than those who use other CSS methodologies, we always know exactly what styles are coming from our classes because there is a 1-to-1 mapping. 4. The breadth of utility classes allows us to write 95-100% of our CSS using just utilities. This encourages a small footprint and very little maintenance overhead. There are other benefits like increased authoring speed and others that I'd like to cover in a more thorough post later. In case you're wondering, what does this look like? Here's an example component from Tailwind's homepage: Now that you've gotten a short introduction, let's get back to why we're here. 2.0 Features Since this is a major version, it needed a major launch which included not only a teaser and trailer, but also a whole new website and docs! If this isn't maximalist launching, I don't know what is! Let's see the most exciting changes with a few examples that you couldn't make without customizing version 1. Theming and Design I don't think it would be a major version launch if Tailwind didn't touch these two topics because they are the main pillars of why we use Tailwind in the first place. They didn't just touch these though, they brought big changes. Colors With regards to theming and design, the biggest of change is colors. The Tailwind team created an all new color palette which includes 17 colors and 5 grays. This is a huge jump in expressiveness for designs, especially with alternate grays because the old gray was on the cool side. The new sets add two cooler grays and two warmer grays so you can have something that jives better with your design's palette. Color docs Dark Mode Another big change is the new support for dark mode. Tailwind doesn't force one color to another for dark mode, but instead let's you control which colors get applied through the new modifier dark:. This will apply whatever style follows whenever a .dark class is above it. Dark Mode docs New Breakpoint and Extended Scales In an effort to support even more refined designs, the team has added support for a larger breakpoint 2xl which now allows more specification for larger layouts. I often added this as a custom breakpoint. Breakpoint docs They also added extended scales for spacing, typography, and opacity. For spacing, a few half values were added near the bottom for nudging smaller spacing layouts. For typography, a few values were added to the top end for larger hero text. Opacity had the largest set of adds with steps every 10, and a few half steps at the ends. Spacing, typography, and opacity docs. A Practical Look at New Theming Changes I've put together a small demo of landing page hero to show off the new changes. Try toggling the dark class to see dark mode: New and Extended Utilities It wouldn't be Tailwind without utilities, and in a major launch, we're going to see some new ones. I think both the new forms plugin and ring utilities are big wins for accessibility because they encourage good practice. The new colors are sure to help us get good contrast! Forms Forms have always been a struggle to style. We often resort to hacks and custom implementations with Javascript to be able to make them look good and match our designs. The Tailwind team has worked hard to make standard HTML form inputs able to be styled without custom elements or hacks. There are some limitations here, but I think the result looks great. Forms plugin docs Ring Another issue that we often encounter when auditing the accessibility of our apps is showing clear focus indicators. The default web version of this is accomplished through the outline property. Unfortunately, outline ignores border-radius and isn't quite as customizable as something like box-shadow. The new ring utility will use box-shadow and that will work alongside the standard shadow utilities in Tailwind without overriding them. Our focus indicators can now look as good as the rest of our UI! 💅 Ring docs Text Overflow A small change, but still helpful: you can now specify overflow-ellipsis and overflow-clip to control text overflow in a multi-line scenario. Text-overflow docs Default Line-heights Something that I feel I'm always tweaking is line-height. I think good line-heights can make or break type styles. In an effort to help improve the quality of typography in Tailwind, the team has shipped default line-heights for each text size, and like always, you can change that with the leading utility. Font-size docs Practical Example of New Utilities I've put together a small form component to show off the new utilities: Internal Mechanics Changes Quite a few internal changes have taken place, one major one being that Tailwind dropped support for IE11 in 2.0. Given that IE11 has very little representation globally and is officially unsupported by Microsoft, I think this was a good move, especially because it allows them to do even more with the framework. In addition, @apply can now work with all variants which is a huge boost to anyone who heavily used it. Previously, you were required to implement those variants as you would normal CSS, but no longer! Two things changed with config. You can easily extend variant's by adding an extend property to your variants config object. This allows you to just add new variants without having to list out all of the default variants which is just a lot nicer. The other new thing in config is that we can now set default transition timings and easing functions. This allows us to no longer need to specify a duration or easing function if we're just fine with the default, which is probably 80% of the time for me personally. @apply, variant, and transition docs The Road to 2.0 Whether you already use Tailwind or you just got converted above, there's a lot to be excited about with version 2.0. A ton of work went into it and it shows in the quality of what was launched. Creating and maintaining Tailwind quickly became the work of a small company, unfortunately Adam is only so powerful 🤣. He and Steve Schoger needed extra help which they found this year using funds made from their launch of TailwindUI and RefactoringUI. They hired Brad Cornes, Robin Malfait, and Simon Vrachliotis to assist with developing the entire ecosystem that surrounds Tailwind and to push out this new version. The team's desires for the new version were deliberated at length over many of Adam's tweets, Github issues/discussions, and even podcast episodes from Adam's very own Full Stack Radio. Something especially important to point out is the team's desire to stick to semantic versioning. Creating a new version opens up a lot of doors for change which is great, but that door also closes at the same time. Now the Tailwind team has to wait until 3.0 to make changes that will break, but I think they made good calls on what to include in this version. What's Next? If you're as excited as I am, you can head straight to this upgrade guide to dig through what changes you might need to make in your current Tailwind codebase. If you'd like to hear more straight from the creators themselves head on over to their announcement blog post to get an in-depth look at each change. For some of us, it might not be time to change and that's ok. The current version is still a serious piece of craftsmanship with all the same well-thought features. Version 1.9 will be the last update in version one and will retain support for IE11. I think Tailwind has proved to be more future-thinking than most frameworks to date including frameworks in other languages. Their attention to detail and quest for excellence shows up in the quality of their work. As a company, they're just getting started. Hopefully the future holds even more new innovations in this lovely little CSS framework, and beyond!...

Nov 24, 2020

7 mins

Tailwind CSSCSSRelease

The HTML Dialog Element: Enhancing Accessibility and Ease of Use

The HTML Dialog Element: Enhancing Accessibility and Ease of Use Dialogs are a common component added to applications, whether on the web or in native applications. Traditionally there has not been a standard way of implementing these on the web, resulting in many ad-hoc implementations that don’t act consistently across different web applications. Often, commonly expected features are missing from dialogs due to the complexity of implementing them. However, web browsers now offer a standard dialog element. Why use the dialog element? The native dialog element streamlines the implementation of dialogs, modals, and other kinds of non-modal dialogs. It does this by implementing many of the features needed by dialogs for you that are already baked into the browser. This is helpful as it reduces the burden on the developer when making their applications accessible by ensuring that user expectations concerning interaction are met, and it can also potentially simplify the implementation of dialogs in general. Basic usage Adding a dialog using the new tag can be achieved with just a few lines of code. ` However, adding the dialog alone won’t do anything to the page. It will show up only once you call the .showModal() method against it. ` Then if you want to close it you can call the .close() method on the dialog, or press the escape key to close it, just like most other modals work. Also, note how a backdrop appears that darkens the rest of the page and prevents you from interacting with it. Neat! Accessibility and focus management Correctly handling focus is important when making your web applications accessible to all users. Typically you have to move the current focus to the active dialog when showing them, but with the dialog element that’s done for you. By default, the focus will be set on the first focusable element in the dialog. You can optionally change which element receives focus first by setting the autofocus attribute on the element you want the focus to start on, as seen in the previous example where that attribute was added to the close element. Using the .showModal() method to open the dialog also implicitly adds the dialog ARIA role to the dialog element. This helps screen readers understand that a modal has appeared and the screen so it can act accordingly. Adding forms to dialogs Forms can also be added to dialogs, and there’s even a special method value for them. If you add a element with the method set to dialog then the form will have some different behaviors that differ from the standard get and post form methods. First off, no external HTTP request will be made with this new method. What will happen instead is that when the form gets submitted, the returnValue property on the form element will be set to the value of the submit button in the form. So given this example form: ` The form element with the example-form id will have its returnValue set to Submit. In addition to that, the dialog will close immediately after the submit event is done being handled, though not before automatic form validation is done. If this fails then the invalid event will be emitted. You may have already noticed one caveat to all of this. You might not want the form to close automatically when the submit handler is done running. If you perform an asynchronous request with an API or server you may want to wait for a response and show any errors that occur before dismissing the dialog. In this case, you can call event.preventDefault() in the submit event listener like so: ` Once your desired response comes back from the server, you can close it manually by using the .close() method on the dialog. Enhancing the backdrop The backdrop behind the dialog is a mostly translucent gray background by default. However, that backdrop is fully customizable using the ::backdrop pseudo-element. With it, you can set a background-color to any value you want, including gradients, images, etc. You may also want to make clicking the backdrop dismiss the modal, as this is a commonly implemented feature of them. By default, the <dialog> element doesn’t do this for us. There are a couple of changes that we can make to the dialog to get this working. First, an event listener is needed so that we know when the user clicks away from the dialog. ` Alone this event listener looks strange. It appears to dismiss the dialog whenever the dialog is clicked, not the backdrop. That’s the opposite of what we want to do. Unfortunately, you cannot listen for a click event on the backdrop as it is considered to be part of the dialog itself. Adding this event listener by itself will effectively make clicking anywhere on the page dismiss the dialog. To correct for this we need to wrap the contents of the dialog content with another element that will effectively mask the dialog and receive the click instead. A simple element can do! ` Even this isn’t perfect though as the contents of the div may have elements with margins in them that will push the div down, resulting in clicks close to the edges of the dialog to dismiss it. This can be resolved by adding a couple of styles the the wrapping div that will make the margin stay contained within the wrapper element. The dialog element itself also has some default padding that will exacerbate this issue. ` The wrapping div can be made into an inline-block element to contain the margin, and by moving the padding from the parent dialog to the wrapper, clicks made in the padded portions of the dialog will now interact with the wrapper element instead ensuring it won’t be dismissed. Conclusion Using the dialog element offers significant advantages for creating dialogs and modals by simplifying implementation with reasonable default behavior, enhancing accessibility for users that need assistive technologies such as screen readers by using automatic ARIA role assignment, tailored support for form elements, and flexible styling options....

Oct 25, 2024

4 mins

HTMLJavaScript

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Why You Should Use React Query or SWR

Why You Should Use React Query or SWR for External Data

The Problem

Potential Solutions

The Real Solution

Other Problems They Solve

Window Focus Refetching

Request Retry, Revalidation and Polling

Retry Example

Polling Example

Mutation with Optimistic Updates

Prefetching and Background Fetching

Feature Comparison

Conclusion

PS. What about GraphQL? 😂

Hunter Miller

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