ajv VS io-ts

Ajv’s documentation is available here.

In the JavaScript ecosystem you can use the excellent AJV package to validate any JavaScript object against a JSON schema. This is especially useful to ensure that API contracts are maintained when communicating with other services.

Since Fastify supports schema validation with Ajv, the validate module is no longer required on the /shorten route, and we can specify the JSON schema directly on the route. The controllers for both routes will largely remain the same, except that the res parameter is renamed to reply as before:

There's a ton of schema validators out there and most devs have their personal favorite. Mine was zod and is now typebox + ajv.

Good validator library: https://www.npmjs.com/package/ajv

Fastify uses JSON schema to define the validation rules for each route's input payload, which includes the request body, query string, parameters, and headers. The JSON schema is a standard format for defining the structure and constraints of JSON data, and Fastify uses Ajv, one of the fastest and most efficient JSON schema validators available.

In Fastify, JSON schema validation is a built-in feature that allows you to validate the payload of incoming requests before the handler function is executed. This ensures that incoming data is in the expected format and meets the required criteria for your business logic. Fastify's JSON schema validation is powered by the Ajv library, a fast and efficient JSON schema validator.

I think you're looking for a json schema validator like Ajv or Zod.

We can use JSON Schema to validate that our data adheres to a specific structure. Ajv is one popular validator tool for JavaScript applications that allows us to create a schema and then validate JSON against that schema. Here's an example of using Ajv to validate one of the above JSON examples against a schema:

Hey HN,

We're designing a new system and have been kicking the can about JSON object definitions and validation. Soon we need to settle on a system to validate API request bodies and provide helpful error messages.

In the past, I've used JSON Schema ( https://json-schema.org/ ) to define definitions and ajv ( https://ajv.js.org/ ) to validate, but it's a bit verbose and ajv validation errors are more cryptic than I'd like to deal with.

TypeSchema looks interesting. It seems solid (and perhaps stable?), but development hasn't been active for 2 years. It also looks like we'd still need to generate JSON Schema and choose a validation library

Anyway, I'm very curious how others are approaching this problem. How do you organize and generate validations for your type definitions? What libraries do you use to validate and provide human readable error messages?

Thank you!

Qué rico. Si tenés chance meté un proceso de code review fuerte, y para el tema de I/O probá a usar https://github.com/Effect-TS/schema ó https://github.com/gcanti/io-ts que les da una solución obvia al tema de "tipos para lo que devuelva el backend", aunque es en realidad mucho más capaz que eso.

My fave is still io-ts (https://github.com/gcanti/io-ts/blob/master/docs/index.md) as I find it more flexible than zod at the ingress. The author is also working on the Effect ecosystem which also looks interesting.

I’ve been using io-ts for this and been very happy with it. [1] It’s similar to Swift’s Coding protocol in case you’re familiar.

[1] https://gcanti.github.io/io-ts/

Yeah, there are a few new concepts and it's not the easiest to pick up right away. The best introduction is here on the main documentation page.

We use https://github.com/gcanti/io-ts/blob/master/Decoder.md which has a very similar interface. It can even be used to mutate the data using https://github.com/gcanti/io-ts/blob/master/Decoder.md#the-parse-combinator.

A good way to significantly improve the reliability of your app is via improving type-safety by moving away from using any to unknown. One relevant example could be when you type your backend responses and when stringifying JSON to using unknown combined with some sort of runtime type checking. It can be done either by using built-in functionality like type guards or using an external library like io-ts, zod or yup.

Usage of TypeBox is similar with io-ts and zod, but it is much powerful and faster than them. Also, TypeBox can generate JSON schema very easily. Therefore, if you're looking for a validator library for new project and not suffering from legacy codes, I think TypeBox would be much better choice than io-ts and zod. TypeBox can totally replace them.

This check can be done with different libraries like: io-ts, typebox, or zod. These libraries allow you to create objects that represent your typescript definitions. Then, these objects can be used at runtime to validate the received data, in addition, you can also convert this object to a Typescript definition to have all the benefits of using typescript. These objects can be called schema validations because they are responsible for the data validation.