I was building an API recently when I hit a familiar wall. My TypeScript interfaces were perfect. My code compiled without a single error. Then, a user sent a malformed JSON payload, and everything broke. TypeScript’s safety, it turns out, vanishes at runtime. The types are just suggestions to your editor. They don’t exist when your server is running. This gap between compile-time confidence and runtime reality is where bugs and security issues thrive. That’s why I started combining TypeScript with Joi. It’s not about choosing one over the other; it’s about making them work together to build truly robust applications.
Think about it. Where does your data come from? A user filling out a form. A request from a mobile app. A third-party API. A database. TypeScript has no way to check this incoming data. It assumes the world matches your interface definitions. Joi doesn’t assume. It checks. It validates the actual data against a set of rules you define. By using them together, you get the best of both worlds: intelligent code completion during development and a strong safety net when your application is live.
Let’s look at a basic example. Imagine you’re creating a user registration endpoint. You might start with a TypeScript interface.
interface UserRegistration {
email: string;
password: string;
age?: number;
}This is helpful for writing your service logic. But what stops someone from sending { email: 123, password: null }? Nothing. Now, let’s add a Joi schema for the same data shape.
import Joi from 'joi';
const userRegistrationSchema = Joi.object({
email: Joi.string().email().required(),
password: Joi.string().min(8).required(),
age: Joi.number().integer().min(13).optional()
});This schema actively validates. Is email a valid email string? Is password at least 8 characters? Is age, if provided, an integer over 13? Joi will check all of this. You can use this schema in an Express middleware to validate the request body before your controller even runs.
But here’s the first challenge. I now have two sources of truth: the UserRegistration interface and the userRegistrationSchema object. If I update one, I must remember to update the other. This is where mistakes happen. Have you ever fixed a bug because your validation logic didn’t match your types?
This is the problem that made me search for a better way. The goal is a single source of truth. You define your validation rules once, and your TypeScript types are generated from them. A library called joi-to-typescript does exactly this. You write your Joi schema, and it produces the corresponding TypeScript interface automatically. No more drift.
Let’s set up a practical pattern. I often create a dedicated file for my schemas. Using joi-to-typescript, I can export both the schema and the generated type.
// schemas/user.ts
import Joi from 'joi';
export const userSchema = Joi.object({
id: Joi.string().uuid().required(),
name: Joi.string().min(2).required(),
email: Joi.string().email().required(),
preferences: Joi.object({
newsletter: Joi.boolean().default(true),
theme: Joi.string().valid('light', 'dark').default('light')
}).default({})
});
// This type is generated automatically from userSchema
export type User = import('joi-to-typescript').GetType<typeof userSchema>;Now, my User type is always in sync with userSchema. If I add a new required field to the schema, the type updates immediately. My business logic uses the User type, and my validation layer uses the userSchema. They cannot disagree.
How do you actually use this in a web server? In an Express application, I create a simple validation middleware. This middleware uses the Joi schema to check the incoming request. If the data is valid, it proceeds. If not, it sends a detailed error response.
import { Request, Response, NextFunction } from 'express';
import { userSchema } from './schemas/user';
export function validateBody(schema: Joi.ObjectSchema) {
return (req: Request, res: Response, next: NextFunction) => {
const { error, value } = schema.validate(req.body);
if (error) {
return res.status(400).json({ error: error.details[0].message });
}
// Replace the request body with the validated (and possibly coerced) value
req.body = value;
next();
};
}
// In your route definition
app.post('/api/users', validateBody(userSchema), (req, res) => {
// At this point, TypeScript knows req.body matches the User type
// and Joi has guaranteed its shape at runtime.
const newUser: User = req.body;
// ... save user logic
});Notice what happens. The route handler receives a req.body that Joi has already validated and sanitized. TypeScript can confidently treat it as a User object. This flow catches bad data at the edge of your application, before it can cause problems deeper in your codebase.
The benefits extend beyond APIs. I use the same pattern for environment configuration. Instead of just declaring process.env.SOME_KEY, I define a Joi schema that validates all required environment variables are present and correct when the application starts. It fails fast with a clear message, rather than crashing later with a cryptic “undefined” error.
Is there a performance cost? Yes, there is. Joi validation adds overhead. For most applications, this is negligible compared to the safety it provides. For extremely high-throughput endpoints, you might profile and optimize, but always validate your data somewhere. The cost of a security breach or corrupted data is almost always higher than the cost of validation.
Some developers ask if they should use TypeScript’s newer runtime type-checking features instead. Tools like zod or io-ts are excellent and designed specifically for this dual purpose. They are great choices. I stick with Joi because it’s mature, feature-rich, and its error messages are incredibly clear for end-users. The principle remains the same: bridge the static and runtime worlds.
Start small. Pick one endpoint or one data model in your project. Define a Joi schema for it, generate the TypeScript type, and integrate the validation. You’ll immediately feel the difference. Your code will be more predictable. Your error handling will be cleaner. You’ll spend less time debugging issues caused by unexpected data shapes.
The peace of mind this approach brings is significant. You stop guessing about your data. You know. TypeScript gives you confidence while you write code. Joi gives you confidence while your code runs. Together, they form a complete safety system. Try it on your next project. You might find, as I did, that you never want to build an API without it.
Did this approach solve a problem for you? Have you tried similar patterns with other validation libraries? Share your thoughts in the comments below—I’d love to hear about your experiences. If you found this useful, please like and share it with other developers who might be wrestling with the same runtime-type divide.
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