Javascript

Quick Guide to Playwright Fixtures: Enhancing Your Tests

Published Jul 26, 2024

Updated Jul 30, 2024

2 min read

Introduction

Following our recent blog post on migrating E2E tests from Cypress to Playwright, we've identified opportunities to enhance our test scripts further. In this guide, we'll delve into the basics of Playwright fixtures, demonstrating their utility and flexibility in test environments.

Playwright fixtures are reusable components that set up and tear down the environment or conditions necessary for tests. They are crucial for writing clean, maintainable, and scalable tests. Fixtures can handle tasks like opening a browser, initializing a database, or logging into an application—actions you might need before running your tests.

As a practical example, we'll revisit one of the tests from our previous post, enhancing it with a new fixture to streamline the testing process and significantly improve maintainability. This post is designed to provide the foundational skills to integrate fixtures into your testing workflows effectively, giving you the confidence to manage and maintain your test scripts more efficiently.

Creating Our First Fixture

To illustrate the power of fixtures in Playwright, let’s consider a practical example from a test scenario in our project. Below is a snippet of a test case from our newsletter page:

test.describe("Newsletter page", () => {
  test("subscribing to email newsletter should show success message", async ({ page }) => {
    await page.goto("/newsletter");
    await page
      .locator(`[data-testid="newsletter-email-input"]`)
      .fill(SIGN_UP_EMAIL_TEST);
    await page.locator(`[data-testid="newsletter-submit-button"]`).click();
    await expect(
      page.getByText(/You subscribed to the newsletter successfully!/).first(),
    ).toBeVisible();
  });
});

This test scenario navigates to the newsletter page, fills in an email, submits the form, and checks for a success message. To optimize our test suite, we'll refactor common actions like navigation, form completion, and submission into reusable fixtures. This approach makes our tests cleaner and more maintainable and reduces redundancy across similar test scenarios.

Implementing the Fixture

Here’s how the fixture looks:

// NewsletterPage.fixture.ts
import type { Page, Locator } from "@playwright/test";

export class NewsletterPage {
  private readonly emailInput: Locator;
  private readonly submitButton: Locator;

  constructor(public readonly page: Page) {
    this.emailInput = this.page.locator(
      `[data-testid="newsletter-email-input"]`,
    );
    this.submitButton = this.page.locator(
      `[data-testid="newsletter-submit-button"]`,
    );
  }

  async goto() {
    await this.page.goto("/newsletter");
  }

  async addEmail(text: string) {
    await this.emailInput.fill(text);
  }

  async submitNewsletter() {
    await this.submitButton.click();
  }
}

This fixture encapsulates the actions of navigating to the page, filling out the email field, and submitting the form. By abstracting these actions, we simplify and focus our test cases.

Refactoring the Test

With the fixture in place, let’s see how it changes our original test file:

import { NewsletterPage } from "playwright/fixtures/NewsletterPage.fixture";

test.describe("Newsletter page", () => {
  let newsletterPage: NewsletterPage;

  test.beforeEach(({ page }) => {
    newsletterPage = new NewsletterPage(page);
  });

  test("subscribing to email newsletter should show success message", async ({ page }) => {
    await newsletterPage.goto();
    await newsletterPage.addEmail(SIGN_UP_EMAIL_TEST);
    await newsletterPage.submitNewsletter();
    await expect(
      page.getByText(/You subscribed to the newsletter successfully!/).first(),
    ).toBeVisible();
  });
});

A beforeEach method to reset the state of the NewsletterPage fixture ensures a clean and consistent environment for each test scenario. This practice is crucial for maintaining the integrity and reliability of your tests.

By leveraging the NewsletterPage fixture, each test within the "Newsletter page" suite starts with a clean and pre-configured environment. This setup improves test clarity and efficiency and aligns with best practices for scalable test architecture.

Conclusion

As we've seen, fixtures are powerful tools that help standardize test environments, reduce code redundancy, and ensure that each test operates in a clean state. By abstracting common setup and teardown tasks into fixtures, we can focus our testing efforts on what matters most, verifying the behavior and reliability of the software we're developing.

Remember, the key to successful test management is choosing the right tools and using them wisely to create scalable, maintainable, and robust testing frameworks. Playwright fixtures offer a pathway towards achieving these goals, empowering teams to build better software faster and more confidently.

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

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About the author(s)

William Mimura
@mimurawil

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

A Look at Playwright Parallelism

A Look at Playwright Parallelism Playwright is an open-source automation library developed by Microsoft, designed for testing web applications across multiple browsers. It enables developers and testers to write scripts that simulate real user actions when interacting with web pages. Playwright supports major browsers like Chrome, Firefox, and Safari, including their headless modes. Recently, it has gained a lot of popularity for its robustness, ease of use, and cross-browser compatibility. In this blog post, we will take a look at one very useful aspect of Playwright: running tests in parallel. Parallelism in Playwright Parallelism in Playwright refers to running multiple test spec files or even test cases within a spec file simultaneously, greatly improving test execution speed. This is achieved by running the tests in worker processes, where each worker process is an OS process, running independently, orchestrated by the test runner. All workers have identical environments, and each starts its own browser instance. Parallel execution is particularly beneficial in continuous integration and continuous deployment (CI/CD) pipelines, reducing overall build and testing times. Playwright's architecture inherently supports parallelism, and most modern test runners integrating with Playwright can leverage this feature. The parallel execution feature makes Playwright a highly efficient tool for large-scale web application testing. Enabling Parallelism By default, if you scaffold the project using npm init playwright@latest, parallelism is already enabled. Assuming that Playwright's configuration includes three browsers and there is a single spec file with two test cases, the total number of tests that Playwright needs to execute is 3x2 = 6. ` Playwright will decide on how many workers to use based on several factors: - Hardware support: Depending on the number of CPU cores and other system resources, the operating system will decide how many processes Playwright can spin up. - The workers property in the playwright.config.ts file. - The --workers argument to the playwright test command. For example, npx playwright test --workers 4. The --workers argument overrides the workers property in the configuration. However, in both cases, the number of workers can go up to the number of processes that Playwright can spin up, as decided by the operating system. Once it has determined the number of workers, and if the number of workers is larger than 1, Playwright will then decide how to split the work between workers. This decision also depends on several factors, such as the number of browsers involved and the granularity of the parallelism, which can be controlled in several ways: - In the test spec file, you can specify whether to run the test cases in parallel. To run tests in parallel, you can invoke test.describe.configure({ mode: 'parallel' }); before your test cases. - Alternatively, you can configure it per project by setting the fullyParallel: true property. - And finally, you can set it globally in the config, using the same property: fullyParallel: true. Therefore, if there is more than one worker and the parallel mode is enabled, Playwright will assign test cases to each worker as they become available. This scenario is ideal because it means that each test is stateless, and the resources are used most efficiently. ` Disabling Parallelism What if, however, the tests are not stateless? Imagine one test changes the global configuration of the app via some sort of administration page, and the configuration affects different parts of the app, like enabling or disabling features. Other tests, which may be testing those features, would be impacted and would report incorrect results. In such cases, you might want to disable parallelism. You can disable any parallelism globally by allowing just a single worker at any time. Following the instructions from the previous sections to configure the number of workers, you can either set the workers: 1 option in the configuration file or pass --workers=1 to the command line. ` Let's have a look at our test output in this case: ` Now, compare the time it took with one worker versus the time it took with four workers. It took 8.2 seconds with one worker compared to only 3.9 seconds with multiple workers. That might be an inefficient usage of resources, of course, especially if you have a large test suite and some of the tests are stateless and can be run without impacting other tests. Tweaking Parallelism If you want some tests not to run in parallel, but still want to utilize your workers, you can do that. Again, following the configuration options from the previous sections, you can annotate the entire spec file with test.describe.configure({ mode: 'serial' }); to have the tests run sequentially in that spec file, or use the fullyParallel: false property to run tests sequentially on the project level, or using the same property to run tests sequentially on the global level. This means you can still split the tests between workers, but the tests would be run sequentially within a worker depending on the configuration. For example, let's set the number of workers to 4, but set fullyParallel: false globally. ` The tests need to be run sequentially, but since each browser instance effectively provides an isolated environment, this means tests cannot impact each other between environments, and they are safe to be executed sequentially within an environment. This means setting fullyParallel: false on the global level is not the same as having workers: 1, since the browsers themselves offer an isolated environment for the tests to run sequentially. However, since we only have 3 environments (3 browsers), we cannot fully utilize 4 workers as we wanted, so the number of workers is 3. Conclusion In conclusion, Playwright's workers are the core of its parallelism capabilities, enabling tests to run concurrently across different environments and browsers with efficiency. Through its many configuration properties, Playwright allows you to configure parallelism at multiple levels, offering a granular approach to optimizing your test runs. Beyond just executing tests in parallel on a single machine, Playwright's parallelism can even extend to splitting work across multiple machines through sharding, significantly enhancing the scalability of your testing. We hope this blog post was useful. For those interested in delving deeper into the world of Playwright and its powerful features, we've also recently released a JS Drop titled Awesome Web Testing with Playwright, and we also hosted Debbie O'Brien from Microsoft in a Modern Web episode....

Mar 20, 2024

5 mins

JavaScriptPlaywrightTesting

How to create and use custom GraphQL Scalars

How to create and use custom GraphQL Scalars In the realm of GraphQL, scalars form the bedrock of the type system, representing the most fundamental data types like strings, numbers, and booleans. As explored in our previous post, "Leveraging GraphQL Scalars to Enhance Your Schema," scalars play a pivotal role in defining how data is structured and validated. But what happens when the default scalars aren't quite enough? What happens when your application demands a data type as unique as its requirements? Enter the world of custom GraphQL scalars. These data types go beyond the conventional, offering the power and flexibility to tailor your schema to precisely match your application's unique needs. Whether handling complex data structures, enforcing specific data formats, or simply bringing clarity to your API, custom scalars open up a new realm of possibilities. In this post, we'll explore how to understand, create, and effectively utilize custom scalars in GraphQL. From conceptualization to implementation, we'll cover the essentials of extending your GraphQL toolkit, empowering you to transform abstract ideas into concrete, practical solutions. So, let's embark together on the journey of understanding and utilizing custom GraphQL scalars, enhancing and expanding the capabilities of your GraphQL schema. Understanding Custom Scalars Custom scalars in GraphQL extend beyond basic types like String or Int, allowing data to be defined, validated, and processed more precisely. They're instrumental when default types don't quite capture the complexity or specificity of the data, such as with specialized date formats or unique identifiers. The use of custom scalars brings several benefits: * Enhanced Clarity: They offer a clearer representation of what data looks like and how it behaves. * Built-in Validation: Data integrity is bolstered at the schema level. * Flexibility: They can be tailored to specific data handling needs, making your schema more adaptable and robust. With this understanding, we'll explore creating and integrating custom scalars into a GraphQL schema, turning theory into practice. Creating a Custom Scalar Defining a Custom Scalar in TypeScript: Creating a custom scalar in GraphQL with TypeScript involves defining its behavior through parsing, serialization, and validation functions. * Parsing: Transforms input data from the client into a server-understandable format. * Serializing: Converts server data back to a client-friendly format. * Validation: Ensures data adheres to the defined format or criteria. Example: A 'Color' Scalar in TypeScript The Color scalar will ensure that every color value adheres to a valid hexadecimal format, like #FFFFFF for white or #000000 for black: ` In this TypeScript implementation: * validateColors: a function that checks if the provided string matches the hexadecimal color format. * parseValue: a method function that converts the scalar’s value from the client into the server’s representation format - this method is called when a client provides the scalar as a variable. See parseValue docs for more information * serialize: a method function that converts the scalar’s server representation format to the client format, see serialize docs for more information * parseLiteral: similar to parseValue, this method function converts the scalar’s value from the client to the server’s representation format. Still, this method is called when the scalar is provided as a hard-coded argument (inline). See parseLiteral docs for more information In the upcoming section, we'll explore how to incorporate and validate these custom scalars within your schema, ensuring they function seamlessly in real-world scenarios. Integrating Custom Scalars into a Schema Incorporating the 'Color' Scalar After defining your custom Color scalar, the next crucial step is effectively integrating it into your GraphQL schema. This integration ensures that your GraphQL server recognizes and correctly utilizes the scalar. Step-by-Step Integration 1. Add the scalar to Type Definitions: Include the Color scalar in your GraphQL type definitions. This inclusion informs GraphQL about this new scalar type. 2. Resolver Mapping: Map your custom scalar type to its resolver. This connection is key for GraphQL to understand how to process this type during queries and mutations. ` 1. Use the scalar: Update your type to use the new custom scalar ` Testing the Integration With your custom Color scalar integrated, conducting thorough testing is vital. Ensure that your GraphQL server correctly handles the Color scalar, particularly in terms of accepting valid color formats and rejecting invalid ones. For demonstration purposes, I've adapted a creation mutation to include the primaryColor field. To keep this post focused and concise, I won't detail all the code changes here, but the following screenshots illustrate the successful implementation and error handling. Calling the mutation (createTechnology) successfully: Calling the mutation with forced fail (bad color hex): Conclusion The journey into the realm of custom GraphQL scalars reveals a world where data types are no longer confined to the basics. By creating and integrating scalars like the Color type, we unlock precision and specificity in our GraphQL schemas, which significantly enhance our applications' data handling capabilities. Custom scalars are more than just a technical addition; they testify to GraphQL's flexibility and power. They allow developers to express data meaningfully, ensuring that APIs are functional, intuitive, and robust. As we've seen, defining, integrating, and testing these scalars requires a blend of creativity and technical acumen. It encourages a deeper understanding of how data flows through your application and offers a chance to tailor that experience to your project's unique needs. So, as you embark on your GraphQL journey, consider the potential of custom scalars. Whether you're ensuring data integrity, enhancing API clarity, or simply making your schema a perfect fit for your application, the possibilities are as vast as they are exciting. Embrace the power of customization, and let your GraphQL schemas shine!...

Apr 10, 2024

5 mins

GraphQLJavaScript

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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Quick Guide to Playwright Fixtures: Enhancing Your Tests

Introduction

Creating Our First Fixture

Implementing the Fixture

Refactoring the Test

Conclusion

William Mimura

You might also like

How to test React custom hooks and components with Vitest

A Look at Playwright Parallelism

How to create and use custom GraphQL Scalars

Introduction to Zod for Data Validation

Let's innovate together!

You might also like

How to test React custom hooks and components with Vitest

A Look at Playwright Parallelism

How to create and use custom GraphQL Scalars

Introduction to Zod for Data Validation