General

Git Bisect: the Time Traveling Bug Finder

Published Sep 7, 2022

Updated Feb 16, 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This article was written over 18 months ago and may contain information that is out of date.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.

I think it’s safe to say that most of us have been in a situation where we pull down some changes from main and something breaks unexpectedly, or a bug got introduced in a recent deployment. It might not take long to narrow down which commit caused the issue if there’s only a couple of new commits, but if you’re a dozen or more commits behind, it can be a daunting task to determine which one caused it.

But can’t I just check each commit until I find the culprit?

You could just check each commit individually without any special tools until you find the one that caused the issue, but that can be a very slow process. This is not ideal and is analogous to the reason why linear search isn’t as effective as binary search.

As the title suggests, there is a tool that Git provides called “bisect”. What this command does is checks out various commit refs in the tree of the branch you’re currently working in and allows you to mark commits as, “good”, “bad”, or “skip” (invalid / broken build). It does away with the need of having to check each commit individually as it is able to infer if commits are good or bad based on which other commits you have already marked.

Git Bisect in Action

Let’s imagine a hypothetical scenario where some bug was reported for the software we’re working on. Starting a git bisect session usually looks like the following example.

$ git bisect start     # While on main.
$ git bisect bad       # Here we are marking HEAD on main as "bad".
$ git bisect good 6e71b3cac15d32fe2d36c270887df9479c25c640

In this case, the commit hash in this example comes from a commit that I already know works. In the case where you pull down changes, and only then does something break, you can use whatever commit you were at prior before you pulled them down. If it’s an older bug, then you could check an older tag or two to see if it exists there.

Next is the part where we search for the offending commit. Every time you mark a commit, bisect will then navigate to another commit in-between your good and bad starting points using a specialized binary search algorithm.

$ build                # This build succeeds but the bug is present.
$ git bisect bad       # Mark the commit and navigate to another commit.
$ build                # This command succeeds and the bug is not present.
$ git bisect good
$ build                # Build fails for a reason unrelated to the issue.
$ git bisect skip

This is the general workflow you will follow when bisecting for its most basic use case, and these commands will be repeated until there are no more revisions left to review. Bisect will try to predict how many steps are left, and let you know every time you mark a commit. Once you are done, you will be checked into the commit that introduced the regression. This assumes that you marked everything accurately!

After you are done bisecting, you can quickly return to where you started by running git bisect reset.

How Git Bisect Works

Firstly, bisect makes the reasonable assumption that any commits after a bad commit remain bad, and any commits before a good commit remain good. It then continues to narrow down which commit is the cause by asking you to check the middlemost commit, along with some added bias when navigating around invalid commits. Though, that’s not vitally important to understand as a user of the command.

The following graphic shows how bisect moves throughout your branch’s history.

Bisect becomes incredibly useful when dealing with repositories with a lot of history, or when tracking down the cause of a bug that’s been in a codebase for a long time. It makes it possible to mule over hundreds of commits in fewer than a dozen steps! That’s a lot better than going through commits one-by-one or at random.

Limitations

It is worth mentioning that bisect isn’t as useful in cases where commits are very large because they incorporate several different changes all bundled together (e.g. squash merges). In an ideal world, each commit in the main branch’s history can be built, and they will implement or fix one thing and one thing only. But in reality, this isn’t always the case. The skip command is available to help with this scenario, but even with that, it’s possible that a change that caused the bug is in one of those skipped commits; therefore, relying solely on the diff of the determined commit to find the root cause of a bug may be misleading.

Conclusion

Git bisect is a very useful tool that can dramatically decrease the amount of time it takes to identify the cause of a regression. I would also recommend reading the official documentation on git bisect as it’s actually quite informative! There are a lot of good examples in here that demonstrate how you can use the command to its full potential.

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About the author(s)

Jamie Kuppens
I’m a software engineer with an interest in web development and some more esoteric things like emulator development.
@Reshurum @Reshurum

How to build an AI assistant with OpenAI, Vercel AI SDK, and Ollama with Next.js

In today’s blog post, we’ll build an AI Assistant using three different AI models: Whisper and TTS from OpenAI and Llama 3.1 from Meta. While exploring AI, I wanted to try different things and create an AI assistant that works by voice. This curiosity led me to combine OpenAI’s Whisper and TTS models with Meta’s Llama 3.1 to build a voice-activated assistant. Here’s how these models will work together: * First, we’ll send our audio to the Whisper model, which will convert it from speech to text. * Next, we’ll pass that text to the Llama 3.1 model. Llama will understand the text and generate a response. * Finally, we’ll take Llama’s response and send it to the TTS model, turning the text back into speech. We’ll then stream that audio back to the client. Let’s dive in and start building this excellent AI Assistant! Getting started We will use different tools to build our assistant. To build our client side, we will use Next.js. However, you could choose whichever framework you prefer. To use our OpenAI models, we will use their TypeScript / JavaScript SDK. To use this API, we require the following environmental variable: OPENAI_API_KEY— To get this key, we need to log in to the OpenAI dashboard and find the API keys section. Here, we can generate a new key. Awesome. Now, to use our Llama 3.1 model, we will use Ollama and the Vercel AI SDK, utilizing a provider called ollama-ai-provider. Ollama will allow us to download our preferred model (we could even use a different one, like Phi) and run it locally. The Vercel SDK will facilitate its use in our Next.js project. To use Ollama, we just need to download it and choose our preferred model. For this blog post, we are going to select Llama 3.1. After installing Ollama, we can verify if it is working by opening our terminal and writing the following command: Notice that I wrote “llama3.1” because that’s my chosen model, but you should use the one you downloaded. Kicking things off It's time to kick things off by setting up our Next.js app. Let's start with this command: ` After running the command, you’ll see a few prompts to set the app's details. Let's go step by step: * Name your app. * Enable app router. The other steps are optional and entirely up to you. In my case, I also chose to use TypeScript and Tailwind CSS. Now that’s done, let’s go into our project and install the dependencies that we need to run our models: ` Building our client logic Now, our goal is to record our voice, send it to the backend, and then receive a voice response from it. To record our audio, we need to use client-side functions, which means we need to use client components. In our case, we don’t want to transform our whole page to use client capabilities and have the whole tree in the client bundle; instead, we would prefer to use Server components and import our client components to progressively enhance our application. So, let’s create a separate component that will handle the client-side logic. Inside our app folder, let's create a components folder, and here, we will be creating our component: ` Let’s go ahead and initialize our component. I went ahead and added a button with some styles in it: ` And then import it into our Page Server component: ` Now, if we run our app, we should see the following: Awesome! Now, our button doesn’t do anything, but our goal is to record our audio and send it to someplace; for that, let us create a hook that will contain our logic: ` We will use two APIs to record our voice: navigator and MediaRecorder. The navigator API will give us information about the user’s media devices like the user media audio, and the MediaRecorder will help us record the audio from it. This is how they’re going to play out together: ` Let’s explain this code step by step. First, we create two new states. The first one is for keeping track of when we are recording, and the second one stores the instance of our MediaRecorder. ` Then, we’ll create our first method, startRecording. Here, we are going to have the logic to start recording our audio. We first check if the user has media devices available thanks to the navigator API that gives us information about the browser environment of our user: If we don’t have media devices to record our audio, we just return. If they do, then let us create a stream using their audio media device. ` Finally, we go ahead and create an instance of a MediaRecorder to record this audio: ` Then we need a method to stop our recording, which will be our stopRecording. Here, we will just stop our recording in case a media recorder exists. ` We are recording our audio, but we are not storing it anywhere. Let’s add a new useEffect and ref to accomplish this. We would need a new ref, and this is where our chunks of audio data will be stored. ` In our useEffect we are going to do two main things: store those chunks in our ref, and when it stops, we are going to create a new Blob of type audio/mp3: ` It is time to wire this hook with our AudioRecorder component: ` Let’s go to the other side of the coin, the backend! Setting up our Server side We want to use our models on the server to keep things safe and run faster. Let’s create a new route and add a handler for it using route handlers from Next.js. In our App folder, let’s make an “Api” folder with the following route in it: We want to use our models on the server to keep things safe and run faster. Let’s create a new route and add a handler for it using route handlers from Next.js. In our App folder, let’s make an “Api” folder with the following route in it: ` Our route is called ‘chat’. In the route.ts file, we’ll set up our handler. Let’s start by setting up our OpenAI SDK. ` In this route, we’ll send the audio from the front end as a base64 string. Then, we’ll receive it and turn it into a Buffer object. ` It’s time to use our first model. We want to turn this audio into text and use OpenAI’s Whisper Speech-To-Text model. Whisper needs an audio file to create the text. Since we have a Buffer instead of a file, we’ll use their ‘toFile’ method to convert our audio Buffer into an audio file like this: ` Notice that we specified “mp3”. This is one of the many extensions that the Whisper model can use. You can see the full list of supported extensions here: https://platform.openai.com/docs/api-reference/audio/createTranscription#audio-createtranscription-file Now that our file is ready, let’s pass it to Whisper! Using our OpenAI instance, this is how we will invoke our model: ` That’s it! Now, we can move on to the next step: using Llama 3.1 to interpret this text and give us an answer. We’ll use two methods for this. First, we’ll use ‘ollama’ from the ‘ollama-ai-provider’ package, which lets us use this model with our locally running Ollama. Then, we’ll use ‘generateText’ from the Vercel AI SDK to generate the text. Side note: To make our Ollama run locally, we need to write the following command in the terminal: ` ` Finally, we have our last model: TTS from OpenAI. We want to reply to our user with audio, so this model will be really helpful. It will turn our text into speech: ` The TTS model will turn our response into an audio file. We want to stream this audio back to the user like this: ` And that’s all the whole backend code! Now, back to the frontend to finish wiring everything up. Putting It All Together In our useRecordVoice.tsx hook, let's create a new method that will call our API endpoint. This method will also take the response back and play to the user the audio that we are streaming from the backend. ` Great! Now that we’re getting our streamed response, we need to handle it and play the audio back to the user. We’ll use the AudioContext API for this. This API allows us to store the audio, decode it and play it to the user once it’s ready: ` And that's it! Now the user should hear the audio response on their device. To wrap things up, let's make our app a bit nicer by adding a little loading indicator: ` Conclusion In this blog post, we saw how combining multiple AI models can help us achieve our goals. We learned to run AI models like Llama 3.1 locally and use them in our Next.js app. We also discovered how to send audio to these models and stream back a response, playing the audio back to the user. This is just one of many ways you can use AI—the possibilities are endless. AI models are amazing tools that let us create things that were once hard to achieve with such quality. Thanks for reading; now, it’s your turn to build something amazing with AI! You can find the complete demo on GitHub: AI Assistant with Whisper TTS and Ollama using Next.js...

Sep 27, 2024

8 mins

AINextJSJavaScript

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

CSS Hooks: A new way to style your React apps

In the world of web development, the management of styles has always been a crucial aspect of building modern and responsive user interfaces. With the rise of CSS in JS libraries like Material UI and Chakra, developers have started creating dynamic and reusable styles using JavaScript; however, the performance implications of these libraries have led to the exploration of alternative solutions. One such solution is CSS Hooks, a library that offers a different approach to managing styles in web applications. The Problem with CSS in React and CSS in JS Libraries In React applications, you typically write styles directly with the style prop by applying classes with className, or if you’re using a framework like Material UI or Chakra then you may use the sx attribute, which is more powerful than the baseline attributes. There are some performance concerns in the case of the sx attribute specifically. The framework has to dynamically generate classes with your styles and apply them to the document whenever it is used. This can become an issue with large and complex applications as your styles will be regularly regenerated on the fly as the user navigates through the application. This has led developers to seek alternative solutions that offer similar flexibility but with better performance. Introduction to CSS Hooks CSS Hooks improve upon the aforementioned issues by pre-generating your CSS using configuration hooks. The styles that these hooks generate remain static during the lifetime of the application and are reused wherever possible. CSS variables are utilized under the hood whenever dynamic behavior is needed. By dynamic behavior, I’m referring to advanced CSS features such as pseudo-class selectors and media queries, which are both traditionally unavailable for use with inline styles. CSS Hooks utilize an interesting trick under the hood to accomplish this, which we will now explore. The Power of CSS Variables and the Fallback Trick As mentioned before, CSS variables are utilized in order to support using advanced dynamic behavior such as pseudo-selectors in our inline styles. CSS variables are core to something known as the “fallback trick”. The gist of how the fallback trick works is to have the pseudo-selector or media query toggle the state of a variable between initial and an empty invalid value, and then we put the value that we actually want to toggle as the “fallback” value in the reference to the CSS variable inside of the inline style itself. Changing this fallback value inside of an inline style essentially allows us to control the values used by the pseudo-selector without having to regenerate linked stylesheets as we only have to change the inline style attribute. This is best explained with an example: ` This plain HTML and CSS code effectively allows us to utilize the focus pseudo-selector on any element that we want using inline styles only. This is what CSS Hooks effectively does in the background when you use dynamic styles. No nasty re-renders and mucking with stylesheets needed! How to use CSS Hooks Now that we know how CSS Hooks fundamentally work, let’s try using them. The library itself is much easier to use than the aforementioned example. Installation and usage of CSS Hooks is easy. Firstly, we must install the @css-hooks/react package into a React project with the package manager of your choice (npm, pnpm yarn, etc). Once that’s done, all that’s left is a little bit of configuration. You have to first use a utility function called createHooks that is exported from the package we just installed. The result of that function returns a couple of variables in an array that can be deconstructed, the first being a stylesheet in the form of a string, and the second being a function. ` I recommend putting this in a separate file that can be imported from multiple other places in the project as this is where we will be configuring hooks across the project. We’re exporting both hooks and css, and both are important. We need to include hooks into the DOM as it contains the styles of the hooks that we’ll be referencing in our components. In your root component you need to add the following tag so that the generated CSS can be used. ` How this is done may vary based on the libraries that you are using. After that’s done, you can integrate CSS Hooks into your components while writing inline styles mostly just like you would do normally, with the only change being the addition of an additional call to the css function that we exported earlier: ` Now that the core boilerplate is set up, your components can now utilize CSS Hooks in theory; however we need to actually add some hooks to start with before we can use them, or else we’ll just be limited to basic styles in the same way that React inline styles are. Make your own hooks We can define our hooks inside of the css-hooks file that we created earlier, the same one that exports the css helper function that gets CSS Hooks working with our components. Hooks are defined in the options parameters that are currently empty. Defining a simple hook that allows us to define inline hover styles for a component is very easy and can be done as follows: ` Although both the key and the value in this example are the same, they both serve different purposes. The key is the name of the hook that we reference when we want to use it, while the value is the spec. The spec is the actual CSS selector that we’re writing. The name can be anything that you want it to be so long as it doesn’t clash with any existing style properties or hooks. Recommended hooks Although making your own hooks may be necessary at times, there is another repository provided by the CSS Hooks authors that adds a way to generate many useful hooks called @css-hooks/recommended. I highly recommend using this library to generate your hooks if at all possible, and the usage is pretty simple. Here’s an example showing how you can use it to generate media queries, color scheme specific styles and pseudo-selectors: ` Then with that done, these can be utilized as follows: ` We can also modify the hooks configuration to add our own hooks in addition to the recommended hooks by using the spread operator: ` Then that custom hook can be used by simply using its key name: ` Summary CSS Hooks offers a performant alternative to traditional CSS in JS libraries by leveraging CSS variables and providing a unique approach to managing styles in web applications. By pre-generating the stylesheet based on configured hooks and utilizing CSS Variables for dynamic behavior, developers can create reusable styles without the performance overhead associated with dynamic stylesheet generation. If you're interested in exploring CSS Hooks further, be sure to check out @CSSHooks for more information. You can also find some of the examples demonstrated here in a working app in our demos repository. Thank you for reading!...

Feb 28, 2024

5 mins

CSSReact

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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Git Bisect: the Time Traveling Bug Finder

But can’t I just check each commit until I find the culprit?

Git Bisect in Action

How Git Bisect Works

Limitations

Conclusion

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