Javascript

Mapping Returned HTTP Data with RxJS

Published Jan 4, 2021

Updated Mar 29, 2023

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This article was written over 18 months ago and may contain information that is out of date.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

In frontend development, it's likely that you will make API requests to retrieve data from some backend resource. However, some backends are set up in such a way that they either send too much data or not enough data back.

When we combine what we know already about Reactive Programming and RxJS, we can handle these situations in a very elegant manner using some useful operators that RxJS provides us.

In this article, we will look at handling both scenarios and look at four operators in particular: map, mergeMap, concatMap and switchMap. We will also provide links to some StackBlitz examples so you can see these live.

For fun, we'll use the Star Wars API to fetch data and use Angular as our Frontend framework of choice as its HTTP library has Reactive Bindings out of the box.

Handling too much data (part 1)

Ok, let's set up the scenario. We want to find out some details about Luke Skywalker such as his name, birth year, height, weight, and eye color.

With Angular, we can do this by simply making an HTTP request:

this.http.get("https://swapi.dev/api/people/1")
    .subscribe(response => console.log(response));

If we do this, we do get back the info we need, but we also get back a lot of info we don't need such as when the API entry was created and edited, Luke's species, his Vehicles etc. Right now, we don't care about those details.

Let's see how we could use RxJS's map operator to only return the data we want.

this.http.get("https://swapi.dev/api/people/1")
    .pipe(map(response => ({
        name: response.name,
        birthYear: response.birth_year,
        height: Number(response.height),
        weight: Number(response.mass),
        eyeColor: response.eye_color
    })))
    .subscribe(luke => console.log(luke))

We can see from the example above that it's super easy to only pull the values you need from the response object!

Note

You can also see how we managed to map some of the fields in the response from snake_case to camelCase which is more of a convention in JavaScript as well as mapping some fields in the response to a new name (mass -> weight). We can also convert some types to other types, such as mass as string to weight as number. This can be super helpful to keep the domain language of your frontend codebase intact.

Live Example

You can check this code out in action over at the live example here on StackBlitz. Feel free to fork it and play with it.

Handling too much data (part 2)

Another common use case of having too much data is search results. Some times we only want to display the first result from a bunch of search results.

Let's set up a new scenario.

We'll create a typeahead search that only shows the most relevant search result. We want to request search results after every key press the user makes; however, we also don't want to make requests that are not needed. Therefore, we want to cancel any inflight requests created as the user types.

For this scenario, we'll be using a mix of the map and switchMap operators.

We'll also use Angular's Reactive Forms Module to provide us with some reactive bindings.

Our code for this is very simple:

this.searchResult$ = this.search.valueChanges.pipe(
    // We get the search term the user has typed
    // And use switchMap to cancel any inflight requests
    // then create a new request and switch to that
    // Observable stream
    switchMap(term =>
      this.http.get<any>(`https://swapi.dev/api/people/?search=${term}`)
    ),
    // Next we check that there is results, if so we pick the first one
    // If not we create an object to show there are no results
    map(response =>
      response.count > 0 ? response.results[0] : { name: "No results" }
    ),
    // We then map the full response data down to only the fields we 
    // care about.
    map(
      response =>
        ({
          name: response.name,
          birthYear: response.birth_year,
          height: Number(response.height),
          weight: Number(response.mass),
          eyeColor: response.eye_color
        } as PeopleData)
    )
);

It's that simple to create an eager typeahead search with Angular and RxJS.

You can see the live example of this here on StackBlitz.

Handling too little data

We've looked at how to handle too much data in the API response with RxJS, but how about when we have too little data?

We can build this scenario out perfectly with the Star Wars API. Let's say we want to see what films the characters we are searching for appear in. The API response for the characters does contain what films they are in, but it does not give us details, just a URL that we can send a new request to get that film's data. This is also an Array of films so we may want to get the details for all the films they appear in at this time.

Let's see how we can transform our search code to allow us to fetch more data related to the results and map it into the final object that we use to render the data.

this.searchResult$ = this.search.valueChanges.pipe(
    // Again we take the search term and map to a new api request
    switchMap(term =>
      this.http.get<any>(`https://swapi.dev/api/people/?search=${term}`)
    ),
    // Now we use mergeMap as we do not need cancellation
    mergeMap(response =>
        // We use from to iterate over each film for the character
        from(response.films).pipe(
            // We need to massage the url to be correct as SW API returns http rather 
            // than https in the character details
            map(
              (film: string) => `${film.substring(0, 4)}${film.substring(4)}`
            ),
            // Now we use concatMap as this will force RxJS to wait for each request
            // to complete before starting the next one, ensuring we have all the 
            // data needed for each film
            concatMap((film: string) => this.http.get<any>(film)),
            // The film API also returns more data than we care about so we map it down
            // to only the fields we care about
            map(film => ({
              title: film.title,
              releaseDate: film.release_date
            })),
            // We then need to collect each of these API responses and map them back into
            // a single array of films
            reduce((films, film) => [...films, film], []),
            // Finally we then map the character data and the film data into one percise
            // object that we care about
            map(
              films =>
                ({
                  name: response.name,
                  birthYear: response.birth_year,
                  height: Number(response.height),
                  weight: Number(response.mass),
                  eyeColor: response.eye_color,
                  films
                } as PeopleData)
            )
        )
    )
);

I highly recommend reading the code above and the comments to understand exactly how to achieve this scenario. This pattern is very powerful, especially for example if you need to iterate over an array of user IDs and fetch the user details associated with those IDs.

There is also a live example of this here on StackBlitz that you can use to try it out.

Conclusion

This is a small introduction to mapping data returned from HTTP requests with RxJS, but hopefully you can see use this as a reference point if you ever need to perform complex data mapping that involves additional API requests.

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)

Colum Ferry
JavaScript Developer and Enthusiast. Really enjoy working with Angular. Married with 2 amazing kids. Powered by Coffee.
@FerryColum @https://github.com/coly010

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

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

Going Reactive with RxJS

RxJS is the perfect tool for implementing reactive programming paradigms to your software development. In general, software development handling errors gracefully is a fundamental piece of ensuring the integrity of the application as well as ensuring the best possible user experience. In this article, we will look at how we handle errors with RxJS and then look at how we can use RxJS to build a simple yet performant application. Handling Errors Our general approach to errors usually consists of us exclaiming "Oh no! What went wrong?" but it's something that is a common occurrence in all applications. The ability to manage errors well without disrupting the user's experience, while also providing accurate error logs to allow a full diagnosis of the cause of the error, is more important than ever. RxJS gives us the tools to do this job very well! Let's take a look at some basic error handling approaches with RxJS. Basic Error Handling The most basic way of detecting and reacting to an error that has occurred in an Observable stream provided to us by the .subscribe() method. ` Here we can set up two different pieces of logic—one to handle non-error emission from the Observable and one to gracefully handle errors emitted by the Observable. We could use this to show a Notification Toast or Alert to inform the user that an error has occurred: ` This can help us minimize disruption for the user, giving them instant feedback that something _actually_ hasn't worked as appropriate rather than leaving them to guess. Composing Useful Errors Sometimes, however, we may have situations wherein we want to throw an error ourselves. For example, some data we received isn't quite correct, or maybe some validation checks failed. RxJS provides us with an operator that allows us to do just that. Let's take an example where we are receiving values from an API, but we encounter missing data that will cause other aspects of the app not to function correctly. ` If we receive a value from the Observable that doesn't contain an ID, we throw an error that we can handle gracefully. _NOTE: Using the throwError will stop any further Observable emissions from being received._ Advanced Error Handling We've learned that we can handle errors reactively to prevent too much disruption for the user. But what if we want to do multiple things when we receive an error or even do a retry? RxJS makes it super simple for us to retry errored Observables with their retry() operator. Therefore, to create an even cleaner error handling setup in RxJS, we can set up an error management solution that will receive any errors from the Observable, retry them in the hopes of a successful emission, and, failing that, handle the error gracefully. ` Once we reach an error, emitting the EMPTY observable will complete the Observable. The output of an error emission above is: ` Usage in Frontend Development RxJS can be used anywhere running JavaScript; however, I'd suggest that it's most predominately used in Angular codebases. Using RxJS correctly with Angular can massively increase the performance of your application, and also help you to maintain the Container-Presentational Component Pattern. Let's see a super simple Todo app in Angular to see how we can use RxJS effectively. Basic Todo App We will have two components in this app: the AppComponent and the ToDoComponent. Let's take a look at the ToDoComponent first: ` Pretty simple, right? It takes an item input and outputs an event when the delete button is clicked. It performs no real logic itself other than rendering the correct HTML. One thing to note is changeDetection: ChangeDetectionStrategy.OnPush. This tells the Angular Change Detection System that it should only attempt to re-render this component when the Input has changed. Doing this can increase performance _massively_ in Angular applications and _should_ always be applicable to _pure_ presentational components, as they should only be rendering data. Now, let's take a look at the AppComponent. ` This is a container component, and it's called this because it handles the logic relating to updating component state as well as handles or dispatches side effects. Let's take a look at some areas of interest: ` We create a basic local store to store our ToDo items; however, this could be done via a state management system or an API. We then set up our Observable, which will stream the value of our ToDo list to anyone who subscribes to it. You may now look over the code and begin to wonder where we have subscribed to items$. Angular provides a very convenient Pipe that handles this for us. We can see this in the template: ` In particular, it's the (items$ | async) this will take the latest value emitted from the Observable and provide it to the template. It does much more than this though. It also will manage the subscription for us, meaning when we destroy this container component, it will unsubscribe automatically for us, preventing unexpected outcomes. Using a pure pipe in Angular also has another performance benefit. It will only ever re-run the code in the Pipe if the input to the pipe changes. In our case, that would mean that item$ would need to change to a whole new Observable for the code in the async pipe to be executed again. We never have to change item$ as our values are then streamed through the Observable. Conclusion Hopefully, you have learned both about how to handle errors effectively as well put RxJS into practice into a real-world app that improves the overall performance of your application. You should also start to see the power that using RxJS effectively can bring!...

Dec 28, 2020

4 mins

RxJSJavaScript

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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Mapping Returned HTTP Data with RxJS

Handling too much data (part 1)

Note

Live Example

Handling too much data (part 2)

Handling too little data

Conclusion

Colum Ferry

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