ReactJS

Why is My React Reducer Called Twice and What the Heck is a Pure Function?

Published Jun 14, 2024

Updated Jun 14, 2024

3 min read

Why is My React Reducer Called Twice and What the Heck is a Pure Function?

In a recent project, we encountered an interesting issue: our React reducer was dispatching twice, producing incorrect values, such as incrementing a number in increments of two.

We hopped on a pairing session and started debugging. Eventually, we got to the root of the problem and learned the importance of pure functions in functional programming. This article will explain why our reducer was being dispatched twice, what pure functions are, and how React's strict mode helped us identify a bug in our code.

The Issue

We noticed that our useReducer hook was causing the reducer function to be called twice for every action dispatched. Initially, we were confused about this behavior and thought it might be a bug in React. Additionally, we had one of the dispatches inside a useEffect, which caused it to be called twice due to React strict mode, effectively firing the reducer four times and further complicating our debugging process. However, we knew that React's strict mode caused useEffect to be called twice, so it didn't take very long to realize that the issue was not with React but with how we had implemented our reducer function.

React Strict Mode

React's strict mode is a tool for highlighting potential problems in an application. It intentionally double-invokes specific lifecycle methods and hooks (like useReducer and useEffect) to help developers identify side effects. This behavior exposed our issue, as we had reducers that were not pure functions.

What is a Pure Function?

A pure function is a function that:

Is deterministic: Given the same input, always returns the same output.
Does Not Have Side Effects: Does not alter any external state or have observable interactions with the outside world.

In the context of a reducer, this means the function should not:

Modify its arguments
Perform any I/O operations (like network requests or logging)
Generate random numbers
Depend on any external state

Pure functions are predictable and testable. They help prevent bugs and make code easier to reason about. In the context of React, pure functions are essential for reducers because they ensure that the state transitions are predictable and consistent.

The Root Cause: Impure Reducers

Our reducers were not pure functions. They were altering external state and had side effects, which caused inconsistent behavior when React's strict mode double-invoked them. This led to unexpected results and made debugging more difficult.

The Solution: Make Reducers Pure

To resolve this issue, we refactored our reducers to ensure they were pure functions. Here's an extended example of how we transformed an impure reducer into a pure one in a more complex scenario involving a task management application.

Let's start with the initial state and action types:

const initialState = {
  tasks: [],
  completedTasks: [],
  currentTaskIndex: 0,
};

const ActionTypes = {
  ADD_TASK: "ADD_TASK",
  COMPLETE_TASK: "COMPLETE_TASK",
  RESET_TASKS: "RESET_TASKS",
  NEXT_TASK: "NEXT_TASK",
};

And here's the impure reducer similar to what we had initially:

import { initialState } from "./reducer-states";

export function reducer(state, action) {
  switch (action.type) {
    case "ADD_TASK":
      state.tasks.push(action.payload); // Direct state modification
      return { ...state };

    case "COMPLETE_TASK":
      state.completedTasks.push(state.tasks[state.currentTaskIndex]); // Direct state modification
      state.tasks.splice(state.currentTaskIndex, 1); // Direct state modification
      return { ...state };

    case "RESET_TASKS":
      return {
        ...state,
 tasks: initialState.tasks,
 completedTasks: initialState.completedTasks,
 currentTaskIndex: initialState.currentTaskIndex,
      };

    case "NEXT_TASK":
      state.currentTaskIndex =
 (state.currentTaskIndex + 1) % state.tasks.length; // Direct state modification
      return { ...state };

    default:
      return state;
  }
}

This reducer is impure because it directly modifies the state object, which is a side effect. To make it pure, we must create a new state object for every action and return it without modifying the original state. Here's the refactored pure reducer:

import { initialState } from "./reducer-states";

export function reducer(state, action) {
  switch (action.type) {
    case "ADD_TASK":
      return {
        ...state,
 tasks: [...state.tasks, action.payload],
      };

    case "COMPLETE_TASK":
      const completedTask = state.tasks[state.currentTaskIndex];
      return {
        ...state,
 tasks: state.tasks.filter(
          (_, index) => index !== state.currentTaskIndex
 ),
 completedTasks: [...state.completedTasks, completedTask],
 currentTaskIndex:
          state.tasks.length > 1
            ? state.currentTaskIndex % (state.tasks.length - 1)
            : 0,
      };

    case "RESET_TASKS":
      return {
        ...state,
 tasks: initialState.tasks,
 completedTasks: initialState.completedTasks,
 currentTaskIndex: initialState.currentTaskIndex,
      };

    case "NEXT_TASK":
      return {
        ...state,
 currentTaskIndex: (state.currentTaskIndex + 1) % state.tasks.length,
      };

    default:
      return state;
  }
}

Key Changes:

Direct State Modification: In the impure reducer, the state is directly modified (e.g., state.tasks.push(action.payload)). This causes side effects and violates the principles of pure functions.
Side Effects: The impure reducer included side effects such as logging and direct state changes. The pure reducer eliminates these side effects, ensuring consistent and predictable behavior.

I've created an interactive example to demonstrate the difference between impure and pure reducers in a React application. Despite the RESET_TASKS action being implemented similarly in both reducers, you'll notice that the impure reducer does not reset the tasks correctly. This problem happens because the impure reducer directly modifies the state, leading to unexpected behavior. Check out the embedded StackBlitz example below:

Conclusion

Our experience with the reducer dispatching twice was a valuable lesson in the importance of pure functions in React. Thanks to React's strict mode, we identified and fixed impure reducers, leading to more predictable and maintainable code. If you encounter similar issues, ensure your reducers are pure functions and leverage React strict mode to catch potential problems early in development. By embracing functional programming principles, you can write cleaner, more reliable code that is easier to debug and maintain.

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About the author(s)

Jan Kaiser
Software engineer passionate about everything web-related. Particularly loves Angular, SCSS, TypeScript, soccer and dachshunds
@honzikec @honzikec

How to test React custom hooks and components with Vitest

Introduction In this guide, we'll navigate through the process of testing React hooks and components using Vitest—a powerful JavaScript unit testing framework. Discover how Vitest simplifies testing setups, providing an optimal solution for both Vite-powered projects and beyond. Vitest is a javascript unit testing framework that aims to position itself as the Test Runner of choice for Vite projects and as a solid alternative even for projects not using Vite. Vitest was built primarily for Vite-powered projects, to help reduce the complexity of setting up testing with other testing frameworks like Jest. Vitest uses the same configuration of your App (through vite.config.js), sharing a common transformation pipeline during dev, build, and test time. Prerequisites This article assumes a solid understanding of React and frontend unit testing. Familiarity with tools like React Testing Library and JSDOM will enhance your grasp of the testing process with Vitest. Installation and configuration Let’s see how we can use Vitest for testing React custom hooks and components. But first, we will need to create a new project with Vite! If you already have an existing project, you can skip this step. ` Follow the prompts to create a new React project successfully. For testing, we need the following dependencies installed: Vitest as the unit testing framework JSDOM as the DOM environment for running our tests React Testing Library as the React testing utilities. To do so, we run the following command: ` Once we have those packages installed, we need to configure the vite.config.js file to run tests. By default, some of the extra configs we need to set up Vitest are not available in the Vite config types, so we will need the vite.config.ts file to reference Vitest types by adding /// reference types=”vitest” /> at the top of the file. Add the following code to the vite.config.ts ` We set globals to true because, by default, Vitest does not provide global APIs for explicitness. So with this set to true, we can use keywords like describe, test and it without needing to import them. To get TypeScript working with the global APIs, add vitest/globals to the types field in your tsconfig.json. ` The environment property tells Vitest which environment to run the test. We are using jsdom as the environment. The root property tells Vitest the root folder from where it should start looking for test files. We should add a script for running the test in package.json ` With all that configured, we can now start writing unit tests for customs hooks and React components. Writing test for custom hooks Let’s write a test for a simple useCounter hook that takes an initial value and returns the value, an increment function and a decrement function. ` We can write a test to check the default return values of the hook for value as below: ` To test if the hook works when we increment the value, we can use the act() method from @testing-library/react to simulate the increment function, as shown in the below test case: ` Kindly Note that you can't destructure the reactive properties of the result.current instance, or they will lose their reactivity. Testing hooks with asynchronous logic Now let’s test a more complex logic that contains asynchronous logic. Let’s write a useProducts hook that fetches data from an external api and return that value ` Now, let’s see what the test looks like: ` In the above example, we had to spy on the global fetch API, so that we can mock its return value. We wrapped that inside a beforeAll so that this runs before any test in this file. Then we added an afterAll method and called the mockRestore() to run after all test cases have been completed and return all mock implementations to their original function. We can also use the mockClear() method to clear all the mock's information, such as the number of calls and the mock's results. This method is handy when mocking the same function with different return values for different tests. We usually use mockClear() in beforeEach() or afterEach() methods to ensure our test is isolated completely. Then in our test case, we used a waitFor(), to wait for the return value to be resolved. Writing test for components Like Jest, Vitest provides assertion methods (matchers) to use with the expect methods for asserting values, but to test DOM elements easily, we will need to make use of custom matchers such as toBeInTheDocument() or toHaveTextContent(). Luckily the Vitest API is mostly compatible with the Jest API, making it possible to reuse many tools originally built for Jest. For such methods, we can install the @testing-library/jest-dom package and extend the expect method from Vitest to include the assertion methods in matchers from this package. ` After installing the jest-dom testing library package, create a file named vitest-setup.ts on the root of the project and import the following into the project to extend js-dom custom matchers: ` Since we are using typescript, we also need to include our setup file in our tsconfig.json: ` In vite.config.ts, we need to add the vitest-setup.ts file to the test.setupFiles field: ` Now let’s test the Products.tsx component: ` We start by spying and mocking the useProducts hook with vi.spyOn() method from Vitest: ` Now, we render the Products component using the render method from @testing-library/react and assert that the component renders the list of products as expected and also the product has the title as follows: ` In the above code, we use the render method from @testing-library/react to render the component and this returns some useful methods we can use to extract information from the component like getByTestId and getByText. The getByTestId method will retrieve the element whose data-testid attribute value equals product-list, and we can then assert its children to equal the length of our mocked items array. Using data-testid attribute values is a good practice for identifying a DOM element for testing purposes and avoiding affecting the component's implementation in production and tests. We also used the getByText method to find a text in the rendered component. We were able to call the toBeInTheDocument() because we extended the matchers to work with Vitest earlier. Here is what the full test looks like: ` Conclusion In this article, we delved into the world of testing React hooks and components using Vitest, a versatile JavaScript unit testing framework. We walked through the installation and configuration process, ensuring compatibility with React, JSDOM, and React Testing Library. The comprehensive guide covered writing tests for custom hooks, including handling asynchronous logic, and testing React components, leveraging custom matchers for DOM assertions. By adopting Vitest, developers can streamline the testing process for their React applications, whether powered by Vite or not. The framework's seamless integration with Vite projects simplifies the setup, reducing the complexities associated with other testing tools like Jest....

Mar 15, 2024

5 mins

ViteReactTesting

Communication Between Client Components in Next.js

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

Jun 21, 2024

5 mins

NextJSReact

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

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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Why is My React Reducer Called Twice and What the Heck is a Pure Function?

Why is My React Reducer Called Twice and What the Heck is a Pure Function?

The Issue

React Strict Mode

What is a Pure Function?

The Root Cause: Impure Reducers

The Solution: Make Reducers Pure

Conclusion

Jan Kaiser

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