Angular

Introduction to NgRx

Published Feb 24, 2021

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This article was written over 18 months ago and may contain information that is out of date.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

Introduction to NgRx

Why

Angular projects are getting more and more complex these days. With handling all the user interaction, application state and accessing this state in every place, it might be necessary but quickly become over complicated. This is why having a one, global app-wide state management system that can be accessible throughout the application may be very useful in modern frontend application. One of the solutions available for Angular applications is NgRx.

What

In this post, I will give you a quick introduction on how to get started using NgRx in you Angular application. I'll base this intro on a simple Angular app where we can display a photo which then can be liked or disliked. You can find the entry point for this application on my GitHub repo. If you want to follow this article's code, please clone the repository and checkout entry_point tag.

git clone git@github.com:ktrz/introduction-to-ngrx.git

git checkout entry_point

After cloning just install all the dependencies

yarn install

and you can see the example app by running

yarn start -o

We will convert this application to use the NgRx state management in just few steps.

How

Key concepts

Let's start with introducing a few key concepts and building blocks of NgRx. NgRx is a state management tool inspired by Redux, so the building blocks might be familiar to Redux users. To build and operate on our state, we will need the following basic building blocks:

Store is where state of the application will be stored. It's both an Observable of a state and an Observer of action.
Actions describe all the possible and unique events that can occur within the application. They can occur ie. by user interaction, communication with the server or can be a result of other actions.
Reducers are what binds the actions and the state. All the state changes must occur as result of an action. Those changes are handled by pure functions called reducers. They take the current state and the latest action and compute new state value based on that.
Selectors - to retrieve part of the state that we're interested in, we use pure functions which extract the portion of the state that a given component might be interested in.

NgRx flow

NgRx makes sure that all the interactions in the application follow a certain unidirectional flow. The following diagram illustrates the general flow of the state: NgRx basic flow

Getting started

The easiest way to add NgRx to the project and get started is to use a Angular CLI schematic.

sh ng add @ngrx/store@latest

This command will

add the @ngrx/store package to package.json -> dependencies
yarn install or npm install those dependencies
import the StoreModule.forRoot({}, {}) in your AppModule

Define Actions

The first thing to do after installing the necessary dependencies is to define some actions that can occur in our application. In our case, this can define the following action:

like photo
dislike photo

To put that into code, let's create the following file: src/app/store/photo.actions.ts

import {createAction} from '@ngrx/store';

export const likePhoto = createAction('[Photo List] Like Photo');
export const dislikePhoto = createAction('[Photo List] Dislike Photo');

In this file, we define all the actions (action creators) that can occur in our app. In general, the shape of the action has to be in the following share:

const someAction = {
  type: 'Unique type',
  /* ... some additional properties */
}

The required part is the type property which will be used to identify our action. Other properties are optional and might be used to pass some additional data associated with the event ie. id of the liked/disliked photo.

What we've just defined are action creators. They are functions which return an action of a specific type. To create ie. like action we can do it as following:

const likeAction = likePhoto()

Define Reducer

Now that we have our actions defined, we need a way to consume them and thus make actual changes to the store's state. As described before, this is done using pure functions called reducers. We could define the functions from scratch using ie. switch case statements, but NgRx comes with some handy helper functions which make this process much nicer. Let's create a file to keep our photo reducer in: src/app/store/photo.reducer.ts

import {createReducer, on} from '@ngrx/store';
import {dislikePhoto, likePhoto} from './photo.actions';

export type PhotoState = number;

const initialState: PhotoState = 0;

export const photoReducer = createReducer(
  initialState,
  on(likePhoto, state => state + 1),
  on(dislikePhoto, state => state - 1)
);

As you can see, you import the actions which we've defined previously and use them in our reducer. createReducer is an utility function which creates a reducer with a provided initial state. This gives us a nice quick way to define new reducers as well as great type inference. TypeScript knows the shape of state that photoReducer operates on from the shape of initialState object.

on(...) function can be considered as case statements within a switch. We define that if we encounter a given action, we produce a new state derived from its previous value and optional properties provided within the action.

All this code could be rewritten in the following shape. In my opinion, the construction mentioned above is both more consise and self explanatory as well, so I suggest using it.

export function photoReducer(state = initialState, action: Action): PhotoState {
  switch (action.type) {
    case likePhoto.type:
      return state + 1;
    case dislikePhoto.type:
      return state - 1;
    default:
        return state;
  }
}

After creating a reducer, we need to let our application know that we want to use it. We can define it in our AppModule file and add it to the properties of the StoreModule.forRoot's first param.

  imports: [
    /* ... other modules */
    StoreModule.forRoot({
        photo: photoReducer
      },
      {}
    )
  ],

Define Selectors

Now that we've defined both possible actions and reducer to handle them, we need a way to get the data from the store. To do this, we'll create another pure function called selectors. The responsibility of selectors is to transform the whole state object (which can we a really large object in real life examples) into small bits necessary for a specific part of the application. In our case, we need a way to get the photo information along with the number of likes/dislikes that it currently has.

Let's create another file in our src/app/store directory src/app/store/photo.selectors.ts

import {createSelector} from '@ngrx/store';
import {PhotoState} from './photo.reducer';

const selectPhotoFeature = (state: { photo: PhotoState }) => state.photo;

export const selectPhoto = createSelector(selectPhotoFeature, likes => ({
    title: 'Introduction to NgRx',
    url: 'https://ngrx.io/assets/images/ngrx-badge.png',
    likes
  })
);

We are again using the utility function provided with NgRx - createSelector. Because our state can be a nested tree of objects, this enables us to create more complex selectors based on the already existing ones. In this case, we combine selecting a photo slice of our whole state with selection of the whole photo object (in this case most of its props are static). Whenever a number of likes change, we will get a new instance of the photo, which makes detection of changes super easy and performant.

Use all the building blocks in our component

Now that we've defined all the necessary building blocks, we can finally start using them in our AppComponent. The first thing we need to do is inject the Store service into our component.

import {Store} from '@ngrx/store';

interface AppState {
  photo: PhotoState;
}

@Component({/* ... */})
export class AppComponent {
  constructor(private store: Store<AppState>) {
  }
}

This gives us access to both selecting data from the store (as an Observable) and notifying it about actions happening (by dispatching actions).

Now let's select the state of the photo into a component's property and define methods for dispatching like and dislike actions.

import {select, Store} from '@ngrx/store';
import {selectPhoto} from './store/photo.selectors';
import {dislikePhoto, likePhoto} from './store/photo.actions';

@Component({/* ... */})
export class AppComponent {
  photo$ = this.store.pipe(select(selectPhoto));

  constructor(private store: Store<AppState>) {
  }

  onLike(): void {
    this.store.dispatch(likePhoto());
  }

  onDislike(): void {
    this.store.dispatch(dislikePhoto());
  }
}

To select data from store, we can basically treat it as an Observable and use pipe operator on it. To select data with our previously created selectors, we can use select operator provided by @ngrx/store package.

To dispatch actions and notify the store of it, we can use dispatch() method and feed it with the necessary action (also defined above).

Now all that is left to do is hook up all this state and methods into our component's template.


@Component({
  selector: 'app-root',
  template: `
    <div class="photos">
      <app-photo class="photo" [photo]="photo$ | async" (like)="onLike()" (dislike)="onDislike()"></app-photo>
    </div>
  `,
})

As you can see, we use async pipe to unwrap the photo$ observable and pass a plain object into the [photo] input. The app-photo component provides two outputs: like and dislike which we can react to with our AppComponent's methods.

For clarity purposes, let's define our PhotoComponent in src/app/photo/photo.component.ts (It's already defined within the entry point repo but needs some minor tweaks.)

import {Component, EventEmitter, Input, Output} from '@angular/core';
import {Photo} from './photo';

@Component({
  selector: 'app-photo',
  template: `
    <mat-card class="example-card" *ngIf="photo">
      <mat-card-header>
        <div mat-card-avatar class="example-header-image"></div>
        <mat-card-title>{{photo.title}}</mat-card-title>
      </mat-card-header>
      <mat-card-content>
        <img mat-card-image [src]="photo.url" alt="Intro to NgRx">
      </mat-card-content>
      <mat-card-actions align="end">
        <button mat-icon-button color="warn" (click)="onLike()">
          <ng-container *ngIf="photo.likes > 0; else notLikedButton">
            <mat-icon [matBadge]="photo.likes" matBadgeColor="warn" color="primary">thumb_up</mat-icon>
          </ng-container>
          <ng-template #notLikedButton>
            <mat-icon color="primary">thumb_up_off_alt</mat-icon>
          </ng-template>
        </button>
        <button mat-icon-button (click)="onDislike()">
          <ng-container *ngIf="photo.likes < 0; else notDislikedButton">
            <mat-icon [matBadge]="-photo.likes" matBadgeColor="warn" color="primary">thumb_down</mat-icon>
          </ng-container>
          <ng-template #notDislikedButton>
            <mat-icon color="primary">thumb_down_off_alt</mat-icon>
          </ng-template>
        </button>
      </mat-card-actions>
    </mat-card>

  `,
  styleUrls: ['./photo.component.scss']
})
export class PhotoComponent {
  @Input() photo?: Photo | null;

  @Output() like = new EventEmitter();
  @Output() dislike = new EventEmitter();

  onLike(): void {
    this.like.next();
  }

  onDislike(): void {
    this.dislike.next();
  }
}

and PhotoModule in src/app/photo/photo.module.ts

import { NgModule } from '@angular/core';
import { CommonModule } from '@angular/common';
import {MatBadgeModule} from '@angular/material/badge';
import {MatButtonModule} from '@angular/material/button';
import {MatCardModule} from '@angular/material/card';
import {MatIconModule} from '@angular/material/icon';
import { PhotoComponent } from './photo.component';

@NgModule({
  declarations: [PhotoComponent],
  imports: [
    CommonModule,
    MatCardModule,
    MatButtonModule,
    MatIconModule,
    MatBadgeModule
  ],
  exports: [PhotoComponent]
})
export class PhotoModule { }

Now start your application again and see the result it the browser. You can still like/dislike the photo and the state of the app is stored withing NgRx store.

Conclusion

As you can see, setting up this example required a bit more code to start with. But when the application grows, this additional code pays off as we get one central place where all the state changes are happening. We can see exactly what actions are being dispatched (ie. by using Redux Devtools) and therefore our application is more maintainable.

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

More articles are coming soon! Do not miss any of them and visit the This Dot Blog to be up-to-date.

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

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

About the author(s)

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

End-to-end type-safety with JSON Schema

End-to-end type-safety with JSON Schema I recently wrote an introduction to JSON Schema post. If you’re unfamiliar with it, check out the post, but TLDR: It’s a schema specification that can be used to define the input and output data for your JSON API. In my post, I highlight many fantastic benefits you can reap from defining schemas for your JSON API. One of the more interesting things you can achieve with your schemas is end-to-end type safety from your backend API to your client application(s). In this post, we will explore how this can be accomplished slightly deeper. Overview The basic idea of what we want to achieve is: * a JSON API server that validates input and output data using JSON Schema * The JSON Schema definitions that our API uses transformed into TypeScript types With those pieces in place, we can achieve type safety on our API server and the consuming client application. The server side is pretty straightforward if you’re using a server like Fastify with already enabled JSON Schema support. This post will focus on the concepts more than the actual implementation details though. Here’s a simple diagram illustrating the high-level concept: We can share the schema and type declaration between the client and server. In that case, we can make a request to an endpoint where we know its type and schema, and assuming the server validates the data against the schema before sending it back to the client, our client can be confident about the type of the response data. Marrying JSON Schema and TypeScript There are a couple of different ways to accomplish this: * Generating types from schema definitions using code generation tools * Creating TypeBox definitions that can infer TypeScript types and be compiled to JSON Schema I recommend considering both and figuring out which would better fit your application and workflows. Like anything else, each has its own set of trade-offs. In my experience, I’ve found TypeBox to be the most compelling if you want to go deep with this pattern. Code generation A couple of different packages are available for generating TS types from JSON Schema definitions. * https://github.com/bcherny/json-schema-to-typescript * https://github.com/vega/ts-json-schema-generator They are CLI tools that you can provide a glob path to where your schema files are located and will generate TS declaration files to a specified output path. You can set up an npm hook or a similar type of script that will generate types for your development environment. TypeBox TypeBox is a JSON Schema type builder. With this approach, instead of json files, we define schemas in code using the TypeBox API. The TypeBox definitions infer to TypeScript types directly, which eliminates the code generation step described above. Here’s a simple example from the documentation of a JSON Schema definition declared with TypeBox: ` This can then be inferred as a TypeScript type: ` Aside from schemas and types, TypeBox can do a lot more to help us on our type-safety journey. We will explore it a bit more in upcoming sections. Sharing schemas between client and server applications Sharing our JSON Schema between our server and client app is the main requirement for end-to-end type-safety. There are a couple of different ways to accomplish this, but the simplest would be to set up our codebase as a monorepo that contains both the server and client app. Some popular options for TypeScript monorepos are: PNPM, Turborepo, and NX. If a monorepo is not an option, you can publish your schema and types as a package that can be installed in both projects. However, this setup would require a lot more maintenance work. Ultimately, as long as you can import your schemas and types from the client and server app, you are in good shape. Server-to-client validation and type-safety For the sake of simplicity, let's focus on data flowing from the server to the client for now. Generally speaking, the concepts also apply in reverse, as long as your JSON API server validates your inputs and outputs. We’ll look at the most basic version of having strongly typed data on the client from a request to our server. Type-safe client requests In our server application, if we validate the /users endpoint with a shared schema - on the client side, when we make the request to the endpoint, we know that the response data is validated using the user schema. As long as we are confident of this fact, we can use the generated type from that schema as the return type on our client fetch call. Here’s some pseudocode: ` Our server endpoint would look something like this: ` You could get creative and build out a map that defines all of your endpoints, their metadata, and schemas, and use the map to define your server endpoints and create an API client. Transforming data over the wire Everything looks stellar, but we can still take our efforts a bit further. To this point, we are still limited to serialized JSON data. If we have a created_at field (number or ISO string) tied to our user, and we want it to be a Date object when we get a hold of it on the client side - additional work and consideration are required. There are some different strategies out there for deserializing JSON data. The great thing about having shared schemas between our client and server is that we can encode our type information in the schema without sending additional metadata from our server to the client. Using format to declare type data In my initial JSON Schema blog post, I touched on the format field of the specification. In our schema, if the actual type of our date is a string in ISO8601 format, we can declare our format to be "date-time". We can use this information on the client to transform the field into a proper Date object. ` Transforming serialized JSON Data This can be a little bit tricky; again, there are many ways to accomplish it. To demonstrate the concept, we’ll use TypeBox to define our schemas as discussed above. TypeBox provides a Transform type that you can use to declare, encode, and decode methods for your schema definition. ` It even provides helpers to statically generate the decoded and encoded types for your schema ` If you declare your decode and encode functions for your schemas, you can then use the TypeBox API to handle decoding the serialized values returned from your JSON API. Here’s what the concept looks like in practice fetching a user from our API: ` Nice. You could use a validation library like Zod to achieve a similar result but here we aren’t actually doing any validation on our client side. That happened on the server. We just know the types based on the schema since both ends share them. On the client, we are just transforming our serialized JSON into what we want it to be in our client application. Summary There are a lot of pieces in play to accomplish end-to-end type safety. With the help of JSON Schema and TypeBox though, it feels like light work for a semi-roll-your-own type of solution. Another great thing about it is that it’s flexible and based on pretty core concepts like a JSON API paired with a TypeScript-based client application. The number of benefits that you can reap from defining JSON Schemas for your APIs is really great. If you’re like me and wanna keep it simple by avoiding GraphQL or other similar tools, this is a great approach....

Apr 17, 2024

6 mins

JSONTypeScript

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

Getting Started with Custom Structural Directives in Angular

Introduction Angular comes with many built-in directives. Some of them (eg. NgIf, NgModel or FormControlDirective) are used daily by Angular developers. Those directives can be split into 2 categories: - Attribute directives They can be used to modify the appearance of behavior of Angular components and DOM elements. For example: - RouterLink - NgModel - FormControlDirective - Structural directives They can be used to manipulate the HTML structure in the DOM. Using them, we can change the structure of part of the DOM that they control. For example: - NgIf - NgForOf - NgSwitch In this article, I will focus on the latter. Creating a custom structural directive As I've mentioned above, there are a couple of built-in structural directives in Angular. However, we might come across a case that the ones provided with the framework don't solve. This is where a custom structural directive might help us resolve the issue. But how do we write one? --- All the code examples in this article use the Angular CLI or Nx CLI generated project as a starting point. You can generate a project using the following command, or use Stackblitz starter project. ` --- NgIf directive clone Let's learn the basic concepts by reimplementing the basic features of the NgIf directive. We will call it CsdIf (CSR prefix stands for Custom Structural Directive :)) The structural directive is actually just a regular directive (with some additional syntactic sugars provided by Angular). So we can start with creating a module and empty directive using AngularCLI: ` our new directive should look like this: ` Let's implement the basic functionality of displaying the content if passed value is true. ` To achieve that, we need a couple of elements: - an input that will determine whether to show or hide the content (@Input) - a reference to the template that we want to conditionally display (TemplateRef) - a container that will provide us with access to Angular's view (ViewContainerRef) The input can be just a regular class property with Angular's @Input decorator. The important thing is to use a proper naming convention. For it to work as it does in the example code shown above, we need to name the property the same as the attribute's selector: ` Now our directive has the information whether to display the content or not but we need to also gain access to the TemplateRef and ViewContainerRef instances. We can do that by injecting them via a constructor: ` Now we have all the necessary tools and information to display or hide the content. We can use ViewContainerRef's createEmbeddedView method to display and clear method to remove the content. Important note: To make sure the csdIf property is assigned already, we need to use ngOnInit lifecycle hook. ` With this implementation, the following example already works as expected. ` There is still a problem with this implementation. Let's try to use the following example: ` The "My conditional header" is displayed correctly when the page renders but as soon as we uncheck the showInput, our header doesn't disappear as we would expect. This is because we only check the csdIf input value inside of ngOnInit, but we do not react to the input's changes. To resolve this, we can either use ngOnChanges lifecycle hook or modify the csdIf to be a setter rather than just a property. I will show you the later solution but implementing it using ngOnChanges should be very similar. As a first step, let's modify the csdIf to be a setter, and store its value in a private property show. ` Secondly, when the new csdIf value is set, we need to perform the same logic as we do in ngOnInit. We need to make sure though that we don't render the template twice so we can clear the view first in all cases. ` As a final step, let's refactor to remove the code duplication by extracting the common logic into a method. ` Now, our second example works as expected: ` Handling additional parameters - else template The CsdIf directive shows and hides the content based on the boolean input correctly. But the original NgIf directive allows for specifying an alternative template via the "else" property as well. How do we achieve this behavior in our custom directive? This is where understanding the "syntactic sugar" that stands behind the structural directives is crucial. The following NgIf syntax: ` is actually equivalent to the following syntax: ` This means that the else property is actually becoming ngIfElse input parameter. In general, we can construct the property name by concatenating the attribute following * and the capitalized property name (eg. "ngIf" + "Else" = "ngIfElse""). In case of our custom directive it will become "csdIf" + "Else" = "csdIfElse ` is equivalent to ` By analyzing the "unwrapped" syntax we can notice the the reference to an alternative template is passed via the csdIfElse property. Let's add and handle that property in the custom directive implementation: ` This addition makes our directive much more useful, and allows for displaying content for cases when the condition is true or false. If something is not clear, or you want to play with the example directive please visit the example on Stackblitz. Real life example The above example is very simple, but it gives you tools to create your own custom directive when you need it. If you want to have a look at some real-life custom directive example that we've found useful at This Dot Labs, I suggest checking out our route-config open source library. You can read more about it in one of our articles: - Introducing @this-dot/route-config - What's new in @this-dot@route-config v1.2 Summary In this article, we've learnt how to write a simple custom structural directive that handles additional inputs. We've covered the syntactic sugar that stands behind the structural directive, and how it translates into directive's inputs. In the second part, I will show you how to add some additional functionalities to the custom structural directive and present ways to improve type checking experience for the custom directive's templates. In case you have any questions, you can always tweet or DM me at @ktrz. I'm always happy to help!...

Nov 22, 2021

5 mins

AngularTypeScriptDirectiveWeb Development

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

Let's innovate together!

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

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

Introduction to NgRx

Introduction to NgRx

Why

What

How

Key concepts

NgRx flow

Getting started

Define Actions

Define Reducer

Define Selectors

Use all the building blocks in our component

Conclusion

Chris Trześniewski

You might also like

End-to-end type-safety with JSON Schema

Exploring Angular Forms: A New Alternative with Signals

Getting Started with Custom Structural Directives in Angular

Introduction to Zod for Data Validation

Let's innovate together!

You might also like

End-to-end type-safety with JSON Schema

Exploring Angular Forms: A New Alternative with Signals

Getting Started with Custom Structural Directives in Angular

Introduction to Zod for Data Validation