I’ve been thinking about a problem that keeps coming up in my projects. We write beautiful TypeScript code with perfect types, but then reality hits. A user submits a form, an API sends us data, or we read from a database. Suddenly, all those compile-time guarantees vanish. The data from the outside world doesn’t care about our types. This disconnect between what we promise at compile time and what we actually receive at runtime is a constant source of bugs. It’s why I started combining TypeScript with Joi.
Think about it. TypeScript checks your code before it runs. It’s fantastic for catching mistakes early. But once your application is live, it’s blind. It can’t stop a malformed JSON payload from crashing your server. That’s where Joi comes in. Joi validates data while your application is running. It checks if the incoming information matches the rules you’ve set. By using them together, you get the best of both worlds: safety while writing code and safety while it’s executing.
Why does this matter so much now? Modern applications talk to many services. They accept data from users, other APIs, and IoT devices. Each of these is a potential point of failure. Relying only on TypeScript is like having a great security system that only works during the day. You need protection 24/7. Joi provides that runtime guard.
Let’s look at how this works in practice. You start by defining what your data should look like using Joi’s schema language. This is where you set the rules.
import Joi from 'joi';
const userRegistrationSchema = Joi.object({
username: Joi.string().alphanum().min(3).max(30).required(),
email: Joi.string().email().required(),
age: Joi.number().integer().min(18).max(120),
preferences: Joi.object({
newsletter: Joi.boolean().default(false),
theme: Joi.string().valid('light', 'dark', 'auto').default('auto')
})
});This schema says a user must have a username and a valid email. Age is optional but must be a number between 18 and 120 if provided. It also defines a nested preferences object. This is your contract for incoming data. But here’s the clever part: we can teach TypeScript about this contract.
The manual way is to create a matching TypeScript interface. You look at your Joi schema and write a type that mirrors it.
interface UserRegistrationData {
username: string;
email: string;
age?: number;
preferences?: {
newsletter: boolean;
theme: 'light' | 'dark' | 'auto';
};
}This works, but it’s fragile. What happens when you change the Joi schema? You must remember to update the interface. If you forget, they drift apart. Your types lie to you. This is a common source of subtle bugs. So, how can we keep them in sync automatically?
This is where utility libraries show their value. Tools like joi-to-typescript or typescript-joi can infer TypeScript types directly from your Joi schemas. You define your validation rules once, and your types are generated for you. Let’s see what that looks like.
Imagine you’re building an API endpoint with Express. A POST request comes in to /api/users. You need to validate the request body before you do anything with it. Here’s a clean way to handle it.
import express, { Request, Response } from 'express';
import Joi from 'joi';
import { joiToSwagger } from 'joi-to-swagger'; // A utility for type inference
const app = express();
app.use(express.json());
// 1. Define the Joi Schema
const createUserSchema = Joi.object({
name: Joi.string().required(),
email: Joi.string().email().required(),
password: Joi.string().min(8).required()
});
// 2. Infer the TypeScript type (Conceptual - using a helper)
type CreateUserInput = typeof createUserSchema; // This would be enhanced by a library
app.post('/api/users', async (req: Request, res: Response) => {
// 3. Validate at Runtime
const { error, value } = createUserSchema.validate(req.body);
if (error) {
return res.status(400).json({ error: error.details[0].message });
}
// 4. 'value' is now safe and matches our expected type
// Proceed with business logic, like saving to a database
console.log('Creating user:', value.name);
res.status(201).json({ message: 'User created', userId: 123 });
});In this flow, Joi acts as a gatekeeper. Invalid data never reaches our core logic. The value returned by validate() is guaranteed to match the schema structure. If we use a type inference library, the value variable would have the precise TypeScript type CreateUserInput, giving us autocomplete and error checking in our editor.
But what about more complex rules? Joi excels here. Need to validate that a startDate is before an endDate? Or that a field’s value depends on another field? Joi can handle that with .when() and custom functions. The beauty is that these complex runtime rules can still be reflected in your TypeScript types if the inference is set up correctly.
Consider a configuration object for a job scheduler. Some fields are only required if the job is recurring.
const jobSchema = Joi.object({
name: Joi.string().required(),
type: Joi.string().valid('instant', 'recurring').required(),
interval: Joi.when('type', {
is: 'recurring',
then: Joi.string().pattern(/^(\d+d)?(\d+h)?(\d+m)?$/).required(),
otherwise: Joi.forbidden() // Must not be present if type is 'instant'
})
});Manually writing a TypeScript type for this conditional logic is tricky. An inferred type would correctly make interval an optional property that only exists when type is 'recurring'. This pushes TypeScript’s utility much further.
The real payoff is in maintenance and developer experience. Your validation schema becomes the single source of truth. You change a field from string to number in one place—the Joi schema—and your types update automatically. Your API documentation, generated from the same schema, stays current. The feedback loop tightens, and bugs are caught faster, often the moment invalid data is sent.
Have you ever had to debug an issue where the data in your database didn’t match what your code expected? This combination helps prevent those situations at the entry points of your application.
So, what’s the next step? Start small. Pick one API endpoint in your project. Define a Joi schema for its input. Validate the request body with it. Feel the immediate confidence that the data you’re working with is correct. Then, explore a type inference library to bridge the gap to TypeScript. You’ll begin to see your application not just as static code, but as a dynamic system with defended boundaries.
This approach has fundamentally changed how I build reliable software. It turns runtime uncertainty into compile-time certainty. It makes our applications robust and our code easier to trust. Give it a try on your next feature.
Did you find this breakdown helpful? Have you tried a similar approach? I’d love to hear about your experiences or answer any questions. If this was useful, please consider sharing it with other developers who might be facing the same challenge. Let’s build more resilient software together.
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