Javascript

Functional Programming in TypeScript Using the fp-ts Library: Option

Published Jun 12, 2023

Updated Jun 12, 2023

4 min read

This article was written over 18 months ago and may contain information that is out of date. Some content may be relevant but please refer to the relevant official documentation or available resources for the latest information.

Welcome back to our blog series on functional programming with fp-ts! In our previous post, we introduced the concept of functional programming and how it can be used to write more robust and maintainable code. Also we talked about the building block of fp-ts library: Pipe and Flow operators. Today, we're going to dive deeper into one of the most useful tools in the fp-ts toolbox: the Option type.

What is an Option?

In JavaScript, we often encounter situations where a value may or may not exist. For example, when we try to access a property of an object that may be null or undefined. This can lead to runtime errors and unexpected behavior. The Option type in fp-ts provides a way to handle these situations in a safe and predictable manner.

An Option is a container that can hold either a value or nothing. It is represented by the Option type in fp-ts, which has two constructors: Some and None. The Some constructor is used to wrap a value, while the None constructor represents the absence of a value.

some: The some constructor is used to create an instance of Option when a value is present, or when a computation succeeds. It takes the value as its argument, and wraps it inside of the Option type.

import { some } from 'fp-ts/lib/Option';

const value = 42;

const optionValue = some(value);

console.log(optionValue) // {_tag: ‘some’, value: 42}

none: The none constructor is used to create an instance of Option when a value is absent, or when a computation fails. It does not take any arguments; it simply represents the absence of a value.

import { none } from 'fp-ts/lib/Option';

const optionNone = none;

console.log(optionNone) //{_tag: none}

The some and none constructors help us avoid null and undefined errors by forcing us to handle both cases explicitly using functional programming techniques.

But let's look at some examples:

import { Option, some, none } from 'fp-ts/lib/Option';

interface User {
	name: string;
	email: Option<string>;
	phone: Option<string>;
}

function getUser(id: number): User {
	// some logic to fetch user from database
	if (userExists) {
		return {
			name: userData.name,
			email: some(userData.email),
			phone: some(userData.phone),
			}
	} else {
		return {
			name: '',
			email: none,
			phone: none,
		};
	}
}

const user = getUser(123);

console.log(user.name);

if (user.email._tag === 'Some') {
	console.log(user.email.value);
} else {
	console.log('Email not found');
}

if (user.phone._tag === 'Some') {
	console.log(user.phone.value);
} else {
	console.log('Phone not found');
}

In this example, the getUser function returns a User object that may contain missing data. We use Option to represent the email and phone fields, which may or may not be present. We can then pattern match on the Option to safely handle the case where the field is missing.

Pattern matching is a powerful feature in functional programming that allows developers to match values against a set of patterns and execute corresponding code based on the match.

We are pattern matching Option when we do this in the example above:

if (user.email._tag === 'Some') {
	console.log(user.email.value);
} else {
	console.log('Email not found');
}

Why use Option?

Using Option can help us avoid runtime errors and make our code more robust. By explicitly handling the absence of a value, we can prevent null or undefined errors from occurring. This can also make our code easier to reason about, as we don't have to worry about unexpected behavior caused by missing values.

Option can also help us write more expressive code. By using Some and None instead of null or undefined, we can make our intentions clearer, and reduce the cognitive load on other developers who may be reading our code.

Let's say we have a function findUserById that retrieves a user from a database based on their ID. If the user is found, the function returns the user object. But if the user is not found, it returns null.

function findUserById(id: number): User | null {
	// Code to fetch user from the database
}

const userId = 123;

const user = findUserById(userId);

if (user !== null) {
	// User found, do something with the user object
	console.log(user.name);
} else {
	// User not found
	console.log("User not found");
}

In this example, we explicitly check for null to determine if the user was found or not. This approach can lead to potential runtime errors if we forget to check for null in some parts of our code.

Now, let's rewrite the same example using fp-ts Option:

import { Option, some, none } from 'fp-ts/lib/Option';

function findUserById(id: number): Option<User> {
	// Code to fetch user from the database
} 

const userId = 123;

const userOption = findUserById(userId);

userOption.fold(
	() => {
		// User not found
		console.log("User not found");
	},
	(user) => {
		// User found, do something with the user object
		console.log(user.name);
	}
);

In this example, the findUserById function returns an Option<User>, which can either be some(user) if the user is found, or none if the user is not found.

Instead of manually checking for null, we use the fold method provided by fp-ts Option. If the user is found (some(user)), the second function inside fold is executed (right function), and if the user is not found (none), the second function is executed (left function). This approach ensures that we handle both cases explicitly, avoiding potential null-related errors.

The fold method is a fundamental operation provided by the Option type in fp-ts. It allows us to extract values from an Option instance by providing two functions: one for handling the case when the Option has a value (Some), and another for handling the case when the Option is empty (None).

It's important to note that Option in fp-ts is implemented as a discriminated union, meaning the some and none constructors are different variants of the same type. This enables the compiler to enforce exhaustiveness checking, ensuring that we handle both cases when using fold or other methods that require pattern matching.

Conclusion:

In this post, we've introduced the Option type in fp-ts and shown how it can be used to handle null or undefined values in a type-safe manner. We've also seen how Option can be used to represent optional values in a more self-documenting way. In future posts, we will explore the fold method in more detail with examples, along with other useful Option methods combined with other new fp-ts operators. These techniques will further enhance our functional programming skills and allow us to handle Option instances more effectively.

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About the author(s)

Mattia Magi

Testing a Fastify app with the NodeJS test runner

Introduction Node.js has shipped a built-in test runner for a couple of major versions. Since its release I haven’t heard much about it so I decided to try it out on a simple Fastify API server application that I was working on. It turns out, it’s pretty good! It’s also really nice to start testing a node application without dealing with the hassle of installing some additional dependencies and managing more configurations. Since it’s got my stamp of approval, why not write a post about it? In this post, we will hit the highlights of the testing API and write some basic but real-life tests for an API server. This server will be built with Fastify, a plugin-centric API framework. They have some good documentation on testing that should make this pretty easy. We’ll also add a SQL driver for the plugin we will test. Setup Let's set up our simple API server by creating a new project, adding our dependencies, and creating some files. Ensure you’re running node v20 or greater (Test runner is a stable API as of the 20 major releases) Overview * index.js - node entry that initializes our Fastify app and listens for incoming http requests on port 3001 * app.js - this file exports a function that creates and returns our Fastify application instance * sql-plugin.js - a Fastify plugin that sets up and connects to a SQL driver and makes it available on our app instance Application Code A simple first test For our first test we will just test our servers index route. If you recall from the app.js code above, our index route returns a 501 response for “not implemented”. In this test, we're using the createApp function to create a new instance of our Fastify app, and then using the inject method from the Fastify API to make a request to the / route. We import our test utilities directly from the node. Notice we can pass async functions to our test to use async/await. Node’s assert API has been around for a long time, this is what we are using to make our test assertions. To run this test, we can use the following command: By default the Node.js test runner uses the TAP reporter. You can configure it using other reporters or even create your own custom reporters for it to use. Testing our SQL plugin Next, let's take a look at how to test our Fastify Postgres plugin. This one is a bit more involved and gives us an opportunity to use more of the test runner features. In this example, we are using a feature called Subtests. This simply means when nested tests inside of a top-level test. In our top-level test call, we get a test parameter t that we call methods on in our nested test structure. In this example, we use t.beforeEach to create a new Fastify app instance for each test, and call the test method to register our nested tests. Along with beforeEach the other methods you might expect are also available: afterEach, before, after. Since we don’t want to connect to our Postgres database in our tests, we are using the available Mocking API to mock out the client. This was the API that I was most excited to see included in the Node Test Runner. After the basics, you almost always need to mock some functions, methods, or libraries in your tests. After trying this feature, it works easily and as expected, I was confident that I could get pretty far testing with the new Node.js core API’s. Since my plugin only uses the end method of the Postgres driver, it’s the only method I provide a mock function for. Our second test confirms that it gets called when our Fastify server is shutting down. Additional features A lot of other features that are common in other popular testing frameworks are also available. Test styles and methods Along with our basic test based tests we used for our Fastify plugins - test also includes skip, todo, and only methods. They are for what you would expect based on the names, skipping or only running certain tests, and work-in-progress tests. If you prefer, you also have the option of using the describe → it test syntax. They both come with the same methods as test and I think it really comes down to a matter of personal preference. Test coverage This might be the deal breaker for some since this feature is still experimental. As popular as test coverage reporting is, I expect this API to be finalized and become stable in an upcoming version. Since this isn’t something that’s being shipped for the end user though, I say go for it. What’s the worst that could happen really? Other CLI flags —watch - https://nodejs.org/dist/latest-v20.x/docs/api/cli.html#--watch —test-name-pattern - https://nodejs.org/dist/latest-v20.x/docs/api/cli.html#--test-name-pattern TypeScript support You can use a loader like you would for a regular node application to execute TypeScript files. Some popular examples are tsx and ts-node. In practice, I found that this currently doesn’t work well since the test runner only looks for JS file types. After digging in I found that they added support to locate your test files via a glob string but it won’t be available until the next major version release. Conclusion The built-in test runner is a lot more comprehensive than I expected it to be. I was able to easily write some real-world tests for my application. If you don’t mind some of the features like coverage reporting being experimental, you can get pretty far without installing any additional dependencies. The biggest deal breaker on many projects at this point, in my opinion, is the lack of straightforward TypeScript support. This is the test command that I ended up with in my application: I’ll be honest, I stole this from a GitHub issue thread and I don’t know exactly how it works (but it does). If TypeScript is a requirement, maybe stick with Jest or Vitest for now 🙂...

Nov 29, 2023

5 mins

NodeJSTypeScriptTesting

Understanding Vue's Reactive Data

Introduction Web development has always been about creating dynamic experiences. One of the biggest challenges developers face is managing how data changes over time and reflecting these changes in the UI promptly and accurately. This is where Vue.js, one of the most popular JavaScript frameworks, excels with its powerful reactive data system. In this article, we dig into the heart of Vue's reactivity system. We unravel how it perfectly syncs your application UI with the underlying data state, allowing for a seamless user experience. Whether new to Vue or looking to deepen your understanding, this guide will provide a clear and concise overview of Vue's reactivity, empowering you to build more efficient and responsive Vue 3 applications. So, let’s kick off and embark on this journey to decode Vue's reactive data system. What is Vue's Reactive Data? What does it mean for data to be ”'reactive”? In essence, when data is reactive, it means that every time the data changes, all parts of the UI that rely on this data automatically update to reflect these changes. This ensures that the user is always looking at the most current state of the application. At its core, Vue's Reactive Data is like a superpower for your application data. Think of it like a mirror - whatever changes you make in your data, the user interface (UI) reflects these changes instantly, like a mirror reflecting your image. This automatic update feature is what we refer to as “reactivity”. To visualize this concept, let's use an example of a simple Vue application displaying a message on the screen: ` In this application, 'message' is a piece of data that says 'Hello Vue!'. Let's say you change this message to 'Goodbye Vue!' later in your code, like when a button is clicked. ` With Vue's reactivity, when you change your data, the UI automatically updates to 'Goodbye Vue!' instead of 'Hello Vue!'. You don't have to write extra code to make this update happen - Vue's Reactive Data system takes care of it. How does it work? Let's keep the mirror example going. Vue's Reactive Data is the mirror that reflects your data changes in the UI. But how does this mirror know when and what to reflect? That's where Vue's underlying mechanism comes into play. Vue has a behind-the-scenes mechanism that helps it stay alerted to any changes in your data. When you create a reactive data object, Vue doesn't just leave it as it is. Instead, it sends this data object through a transformation process and wraps it up in a Proxy. Proxy objects are powerful and can detect when a property is changed, updated, or deleted. Let's use our previous example: ` Consider our “message” data as a book in a library. Vue places this book (our data) within a special book cover (the Proxy). This book cover is unique - it's embedded with a tracking device that notifies Vue every time someone reads the book (accesses the data) or annotates a page (changes the data). In our example, the reactive function creates a Proxy object that wraps around our state object. When you change the 'message': ` The Proxy notices this (like a built-in alarm going off) and alerts Vue that something has changed. Vue then updates the UI to reflect this change. Let’s look deeper into what Vue is doing for us and how it transforms our object into a Proxy object. You don't have to worry about creating or managing the Proxy; Vue handles everything. ` In the example above, we encapsulate our object, in this case, “state”, converting it into a Proxy object. Note that within the second argument of the Proxy, we have two methods: a getter and a setter. The getter method is straightforward: it merely returns the value, which in this instance is “state.message” equating to 'Hello Vue!' Meanwhile, the setter method comes into play when a new value is assigned, as in the case of “state.message = ‘Hey young padawan!’”. Here, “value” becomes our new 'Hey young padawan!', prompting the property to update. This action, in turn, triggers the reactivity system, which subsequently updates the DOM. Venturing Further into the Depths If you have been paying attention to our examples above, you might have noticed that inside the Proxy method, we call the functions track and trigger to run our reactivity. Let’s try to understand a bit more about them. You see, Vue 3 reactivity data is more about Proxy objects. Let’s create a new example: ` In this example, when you click on the button, the message's value changes. This change triggers the effect function to run, as it's actively listening for any changes in its dependencies. How does the effect property know when to be called? Vue 3 has three main functions to run our reactivity: effect, track, and trigger. The effect function is like our supervisor. It steps in and takes action when our data changes – similar to our effect method, we will dive in more later. Next, we have the track function. It notes down all the important data we need to keep an eye on. In our case, this data would be state.message. Lastly, we've got the trigger function. This one is like our alarm bell. It alerts the effect function whenever our important data (the stuff track is keeping an eye on) changes. In this way, trigger, track, and effect work together to keep our Vue application reacting smoothly to changes in data. Let’s go back to them: ` Tracking (Dependency Collection) Tracking is the process of registering dependencies between reactive objects and the effects that depend on them. When a reactive property is read, it's "tracked" as a dependency of the current running effect. When we execute track(), we essentially store our effects in a Set object. But what exactly is an "effect"? If we revisit our previous example, we see that the effect method must be run whenever any property changes. This action — running the effect method in response to property changes — is what we refer to as an "Effect"! (computed property, watcher, etc.) > Note: We'll outline a basic, high-level overview of what might happen under the hood. Please note that the actual implementation is more complex and optimized, but this should give you an idea of how it works. Let’s see how it works! In our example, we have the following reactive object: ` We need a way to reference the reactive object with its effects. For that, we use a WeakMap. Which type is going to look something like this: ` We are using a WeakMap to set our object state as the target (or key). In the Vue code, they call this object targetMap. Within this targetMap object, our value is an object named depMap of Map type. Here, the keys represent our properties (in our case, that would be message and showSword), and the values correspond to their effects – remember, they are stored in a Set that in Vue 3 we refer to as dep. Huh… It might seem a bit complex, right? Let's make it more straightforward with a visual example: With the above explained, let’s see what this Track method kind of looks like and how it uses this targetMap. This method essentially is doing something like this: ` At this point, you have to be wondering, how does Vue 3 know what activeEffect should run? Vue 3 keeps track of the currently running effect by using a global variable. When an effect is executed, Vue temporarily stores a reference to it in this global variable, allowing the track function to access the currently running effect and associate it with the accessed reactive property. This global variable is called inside Vue as activeEffect. Vue 3 knows which effect is assigned to this global variable by wrapping the effects functions in a method that invokes the effect whenever a dependency changes. And yes, you guessed, that method is our effect method. ` This method behind the scenes is doing something similar to this: ` The handling of activeEffect within Vue's reactivity system is a dance of careful timing, scoping, and context preservation. Let’s go step by step on how this is working all together. When we run our Effect method for the first time, we call the get trap of the Proxy. ` When running the get trap, we have our activeEffect so we can store it as a dependency. ` This coordination ensures that when a reactive property is accessed within an effect, the track function knows which effect is responsible for that access. Trigger Method Our last method makes this Reactive system to be complete. The trigger method looks up the dependencies for the given target and key and re-runs all dependent effects. ` Conclusion Diving into Vue 3's reactivity system has been like unlocking a hidden superpower in my web development toolkit, and honestly, I've had a blast learning about it. From the rudimentary elements of reactive data and instantaneous UI updates to the intricate details involving Proxies, track and trigger functions, and effects, Vue 3's reactivity is an impressively robust framework for building dynamic and responsive applications. In our journey through Vue 3's reactivity, we've uncovered how this framework ensures real-time and precise updates to the UI. We've delved into the use of Proxies to intercept and monitor variable changes and dissected the roles of track and trigger functions, along with the 'effect' method, in facilitating seamless UI updates. Along the way, we've also discovered how Vue ingeniously manages data dependencies through sophisticated data structures like WeakMaps and Sets, offering us a glimpse into its efficient approach to change detection and UI rendering. Whether you're just starting with Vue 3 or an experienced developer looking to level up, understanding this reactivity system is a game-changer. It doesn't just streamline the development process; it enables you to create more interactive, scalable, and maintainable applications. I love Vue 3, and mastering its reactivity system has been enlightening and fun. Thanks for reading, and as always, happy coding!...

Nov 22, 2023

7 mins

VueJavaScriptTypeScriptWeb Development

"How do I undo my most recent commit?" - Mastering the git reset command

"How do I undo my most recent commit?" - Mastering the git reset command Git is a powerful version control system, but even experienced developers sometimes make mistakes. Whether you've committed changes to the wrong branch, made a typo in your commit message, or simply want to undo recent changes, the git reset command is your go-to solution. In this post, we'll explore how to use git reset to manage your commit history effectively. The Basics of git reset At its core, git reset moves the HEAD and current branch pointer to a specified commit, effectively "resetting" your working directory to a previous state. The basic syntax is: ` Where specifies how to handle changes in the working directory and staging area, and is the commit you want to reset to. Soft vs. Hard Reset: Understanding the Difference Git reset has two main modes: --soft and --hard. Let's explore each: Soft Reset (--soft) A soft reset moves your HEAD to the specified commit but keeps your changes staged. This is useful when you want to undo commits but keep the changes for recommitting. Example: ` This command undoes the last commit, keeping the changes staged. You can now make additional changes or recommit with a new message. Hard Reset (--hard) A hard reset is more drastic. It moves your HEAD to the specified commit and discards all changes after that commit. Example: ` This command undoes the last commit and discards all changes. Be cautious with this command, as it can lead to data loss! Specifying a Commit Hash Instead of using relative references like HEAD~1, you can specify an exact commit hash: ` This resets to the commit with hash 1a2b3c4, keeping changes staged. Using HEAD~n Notation The HEAD~n notation allows you to specify how many commits back you want to reset. For example: ` This resets to three commits before the current HEAD, discarding all changes. Reverting Pushed Changes If you've already pushed commits you want to undo, you should use git revert instead of git reset to avoid rewriting public history. However, if you must use git reset on a shared branch, you'll need to force push: ` Be extremely cautious with force pushing, as it can cause issues for other developers. Generic Usage of git reset git reset can also be used to unstage changes: ` This removes file.txt from the staging area without changing its contents. Conclusion The git reset command is a powerful tool for managing your Git history. Whether you're undoing recent commits, unstaging changes, or completely resetting your working directory, understanding the different modes of git reset can help you navigate tricky situations in your Git workflow. Remember to use git reset with caution, especially when working with shared repositories. When in doubt, create a backup branch before performing any reset operations....

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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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Functional Programming in TypeScript Using the fp-ts Library: Option

What is an Option?

Why use Option?

Conclusion:

Mattia Magi

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"How do I undo my most recent commit?" - Mastering the git reset command

Introduction to Zod for Data Validation

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Testing a Fastify app with the NodeJS test runner

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