Javascript

Getting Started with LitElement and Tailwind

Published Feb 23, 2021

Updated Feb 14, 2023

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This article was written over 18 months ago and may contain information that is out of date.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

Getting Started with LitElement and Tailwind

By taking advantage of LitElement's thin layer over Web Components combined with Tailwind's near infinite offering of CSS utility classes, you can create a beautiful, completely custom, and incredibly performant data-driven user-interface with ease.

Tooling

We will be using a combination of tools to give us as great of a developer experience as possible, as quickly as possible.

RollupJS - Rollup's approach to module bundling relies on the native JavaScript module standard, ES Modules, as opposed to Node's CommonJS module system.
Babel - Babel will allow us to use the latest JavaScript features by compiling down our code to ES5, something older browsers will understand.
TypeScript - Better to have silly errors during development than insanity-inducing errors in production. TypeScript's got our backs by reducing the bugs (accidental features?) we introduce in our code.
ESLint
Prettier

Installing RollupJS and initial configuration

Rollup has an excellent starter-app template that we can use to get going quickly. Clone their repo and we'll use that as our starting point.

Create/Edit/Remove these files:

LICENSE - This is just a prototype, so we don't need this file.
README.md - Again, this is just a prototype, so we don't need this either.
src/update.js - We can remove this.
public/index.html - We can remove the <h1> and the <p> tags. We'll start fresh.
package.json - Remove the date-fns dependency.
src/main.js - Update it so it has a single command to test our config, console.log('Hello, World!')

After that's done, let's test our initial Rollup setup:

yarn install or npm install to install our dependencies
yarn dev or npm dev to run our development environment
Navigate to localhost:5000
Open dev-tools to see our test message

Configuring Babel & TypeScript

To configure Babel and TypeScript, we'll take advantage of Rollup's Babel plugin, and we'll configure babel to compile our TypeScript code.

Install the following dependencies:

@babel/core
@rollup/plugin-babel
@babel/preset-env
typescript
@rollup/plugin-typescript
@babel/preset-typescript

Edit the following files:

src/main.js rename to src/main.ts
rollup.config.js

Import and use the Babel plugin, and set the input file as a .ts file (for TypeScript).

import resolve from '@rollup/plugin-node-resolve';
import commonjs from '@rollup/plugin-commonjs';
import { terser } from 'rollup-plugin-terser';
import typescript from '@rollup/plugin-typescript';
import { babel } from '@rollup/plugin-babel'

// `npm run build` -> `production` is true
// `npm run dev` -> `production` is false
const production = !process.env.ROLLUP_WATCH;

export default {
  input: 'src/main.ts',
  output: {
    file: 'public/bundle.js',
    format: 'es',
    sourcemap: true
  },
  plugins: [
    resolve(),
    commonjs(),
    typescript({
      include: ['src/**/*.ts']
    }),
    babel({
      babelHelpers: 'bundled',
      exclude: "node_modules/**"
    }),
    production && terser({ format: { comments: false } }), // minify, but only  in production
 ]
};

.babelrc - Create and setup Babel configuration file to compile down to a minimum of ES5 code.

{
  "presets": ["@babel/preset-env"]
}

tsconfig/json - Create the TypeScript configuration file with the command yarn tsc --init

Configuring ESLint and Prettier

Install the following dependencies:

eslint
prettier
eslint-config-prettier
eslint-plugin-prettier

Run yarn eslint --init and choose the following options to create .eslintrc.js, ESLint's config file.

enforce code style
JavaScript modules
No Framework
TypeScript
Project runs in the browser
Use Airbnb style guide
config format JavaScript

After this, we can see that our IDE (in my scenario, VS Code) recognizes the ESLint configuration and is throwing some issues my way.

In .eslintrc.js

Add 'plugin:prettier/recommended' to the extends array
Add 'prettier' to the plugins array
Add "prettier/prettier": "error" to the rules object

Create and configure .prettierrc, Prettier's configuration file. This single line is for any Window's developers, preventing any weird end-of-line prettier issues.

{
  "endOfLine": "auto"
}

Using LitElement to make a data-driven User Interface

Now that we have a solid tooling setup, lets code up some UI with LitElement.

LitElement is a thin wrapper around Web Components. It provides us a way to declaratively write our UI as a function of state. A difference though between LitElement and another framework like React or Vue is that LitElement is built on top of the native component model of the web.

Let's code the obligatory 'Hello, World!'

First, install lit-element as a dependency to the project:

yarn add lit-element

To use a web component, we'll define it in src/main.ts, and then we'll use the new component in public/index.html.

src/main.ts

import { LitElement, customElement, html } from "lit-element";

@customElement("app-component")
export default class AppComponent extends LitElement {
  render() {
    return html` <h1>Hello, World!</h1> `;
  }
}

public/index.html

<!doctype html>
<html>

<head lang='en'>
  <meta charset='utf-8'>
  <meta name='viewport' content='width=device-width'>
  <title>LitElement w/ Tailwind</title>

  <style>
    body {
      font-family: 'Helvetica Neue', Arial, sans-serif;
      color: #333;
      font-weight: 300;
    }
  </style>
</head>

<body>
  <app-component></app-component>
  <script type="module" src='bundle.js'></script>
</body>

</html>

You'll most likely run into several ESLint and TypeScript issues. A few to be wary of are:

TypeScript Decorators: The line with @customElement() is a TypeScript feature called a decorator. In order to use them, you have to explicitly turn the feature in in your tsconfig.json.
Expected 'this' to be used by class method 'render': This is an ESLint rule noticing that we aren't using this in the instance method render(). We can tell ESLint to ignore specific functions by adding a rule to our eslintrc.js

After resolving any linting issues you may have, you should have your Hello, World!

Styling components with Tailwind

Now we are going to add Tailwind to our project.

Install the following dependencies:

rollup-plugin-postcss
tailwindcss
postcss
autoprefixer

Run the following command to generate a Tailwind configuration file:

npx tailwindcss init

tailwindcss.config.js

module.exports = {
  purge: [
    './src/**/*.html',
    './src/**/*.js',
  ],
  darkMode: false, // or 'media' or 'class'
  theme: {
    extend: {},
  },
  variants: {
    extend: {},
  },
  plugins: [],
}

Create/Edit the following files:

rollup.config.js

import resolve from '@rollup/plugin-node-resolve';
import commonjs from '@rollup/plugin-commonjs';
import { terser } from 'rollup-plugin-terser';
import typescript from '@rollup/plugin-typescript';
import { babel } from '@rollup/plugin-babel'
import postcss from 'rollup-plugin-postcss' // import postcss plugin

const production = !process.env.ROLLUP_WATCH;

export default {
  input: 'src/main.ts',
  output: {
    file: 'public/bundle.js',
    format: 'es',
    sourcemap: true
  },
  plugins: [
    resolve(),
    commonjs(),
    postcss(), // use postcss plugin
    typescript({
      include: ['src/**/*.ts']
    }),
    babel({
      babelHelpers: 'bundled',
      exclude: "node_modules/**"
    }),
    production && terser({ format: { comments: false } }),
  ]
};

postcss.config.js

module.exports = {
  plugins: {
    tailwindcss: {},
    autoprefixer: {},
  }
}

src/styles.css

@tailwind base;
@tailwind components;
@tailwind utilities;

src/main.ts

import './styles.css' // import the tailwind styles
import { LitElement, customElement, html } from "lit-element";

@customElement("app-component")
export default class AppComponent extends LitElement {
  createRenderRoot() {
    return this; // turn off shadow dom to access external styles
  }
  render() {
    return html` <h1 class="mx-auto my-4 py-4 text-center shadow-lg text-xl w-1/2">Hello, World!</h1> `; // use tailwind css utility classes
  }
}

After our configuration, we now have a working LitElement & Tailwind setup!

Conclusion

After our configuration, we now have a strong base to start creating a beautiful data-driven user interface with powered by both LitElement and Tailwind CSS.

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.

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About the author(s)

Matthew Pagan
@mastapegs @mastapegs

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

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

GraphQL Updates- October 2021

State of GraphQL State of GraphQL brings together core contributors, members of the GraphQL Foundation, and community leaders to talk about the future of GraphQL, and answer audience questions. Sponsors Shout-out to the sponsors of the event, StepZen and Moon Highway! - StepZen > One GraphQL API. All Your Data. Zero Infrastructure. StepZen is a GraphQL-as-a-Service provider, even including a FREE forever pricing tier with 300k calls/month included. - Moon Highway > The cutting edge JavaScript training for engineers of all skill levels Moon Highway is a training company that teaches a lot of courses centered around the JavaScript ecosystem. - JavaScript & Node - React - GraphQL Hosts - Tracy Lee, CEO, This Dot Labs - Eve Porcello, Co-founder & Instructor, Moon Highway Panelists - Uri Goldshtein, Founder, The Guild - Carlos Rufo, Engineering Lead, GraphCMS - Joey Nenni, Web Engineering Lead (GraphQL), PayPal - Janessa Garrow, Developer Experience Engineer, Apollo GraphQL - Tanmai Gopal, Founder, Hasura GraphQL Updates The first portion of the State of GraphQL covered updates from each panelist. The GraphQL Foundation One of the first topics covered by State of GraphQL was The GraphQL Foundation. > The GraphQL Foundation is a neutral foundation founded by global technology and application development companies. The GraphQL Foundation encourages contributions, stewardship, and a shared investment from a broad group in vendor-neutral events, documentation, tools, and support for GraphQL. > > GraphQL.org Uri opened up the conversation by emphasizing how open the foundation is. The GraphQL Foundation has always been open, but it's now easier than ever to become part of the community. The true "meat" of where the GraphQL action is though is in the GraphQL Working Group. There, topics such as changes to the GraphQL spec, ideas for new features, and more are discussed. Also, all the meetings are recorded and even summarized by text! Apollo Odyssey > Odyssey is Apollo's free interactive learning platform. Janessa from the Apollo team introduced us to Apollo Odyssey. Odyssey is Apollo's official learning platform. It was created to improve the developer experience of learning GraphQL. While some people may be able to learn effectively from docs, others may find different learning styles, like those offered by Odyssey, a more effective way of learning. Courses offered range from the basics, like what are queries and resolvers, to the more advanced subjects, such as Apollo Federation. Hasura Tanmai from Hasura spoke next about updates done within Hasura. One of the biggest new features is support for cross-database joins over GraphQL. This update allows the developer to retrieve data from any number of data stores with efficient queries. The next thing being worked on now is end-to-end streaming. PayPal Joey Nenni of PayPal spoke about PayPal's experience with adopting GraphQL. He mentioned that once PayPal supported developers with tooling and sample apps, GraphQL spread like wildfire. Seeing the potential to do more with GraphQL, especially with sharing components, PayPal looked into Apollo Federation about 9 months ago, and as of about 2 weeks ago, they are now live with millions of requests already going through their gateway. Joey also spoke about how GraphQL adoption is really about persistence. It's an iterative process. By making small steps, collecting small wins, and repeating that process, it becomes a lot easier to sell GraphQL. Joey coined the term "Guerilla Warfare" when it comes to finding successful implementations for GraphQL solutions. GraphCMS > GraphCMS gives you instant GraphQL Content APIs to create, enrich, unify, and deliver your content across platforms. GraphCMS recently hosted GraphQL Conf AND has recently raised $10 million in a Series A funding round. With all of this GraphQL focused momentum, GraphCMS is poising itself as a powerful solution for Headless CMS. > The ultimate content platform that enables you to deliver applications at scale. > > Carlos Rufo Defer and Stream One important piece of information that Uri brought to the groups' attention was that defer, stream, live queries, and other features in spec proposals, but not in the actual spec, CAN BE USED SAFELY. Uri noticed a patter in production settings: once people have been given the option to use these new features, like defer, they see how valuable those features can be. If you want to see more information involving these GraphQL directives, you can check out a blog post covering them here. Apollo Federation > Implement a single graph across multiple services > > Apollo Documentation One very popular subject that is commonly brought up in Apollo conversations is Apollo Federation, and the idea of having a unified Graph connecting many services. For PayPal, they spoke about the experiences encountered while transitioning some of their shared logic to a federated graph, while maintaining developer experience. Apollo itself had a very positive experience using Apollo Federation. They ran into the need to unite two services, one written in Kotlin, and one in TypeScript, and have them run under a single gateway. This was a very big win and prevented the need for developers to learn additional languages to support a larger graph. Tanmai had a balanced perspective on Apollo Federation: > Are we federating how the API comes together and presents itself? Or are we federating the execution itself? He shared his experiences with analyzing where exactly a federated graph could be a good fit. For Uri, he spoke about how the most important question is, "Are we actually making the product development faster?" Adopting Apollo Federation may give a different answer depending on the team and work being done. Conclusion GraphQL is still growing rapidly, and as shown in this State of GraphQL, there is a growing thriving community surrounding it. Continue the conversation, no matter your experience level, and check out the GraphQL Working Group. While this was a quick summary of the things spoken about during the State of GraphQL, you can catch the whole video on YouTube. Thank you for enjoying the State of GraphQL!...

Oct 20, 2021

5 mins

GraphQL

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

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