class-validator VS io-ts

You can learn more from the documentation here.

This is the request body when creating a new task. The IsString decorator provided by class-validator rejects all types other than strings, including undefined or null. The task name value object does not consider the possibility of values other than strings.

In this class, we are using the famous class-validator to decorate our request parameters. This way it is more clear how we are going to use our request object and everything.

We add validation using https://github.com/typestack/class-validator.

I will make sure we get this documented.

Also OP can check out class-transformer + class-validator if he want to properly instantiate objects and validate them with precise and verbose output of potential format errors. OP can even go the god tier way by setting up a custom RxJS operator that does that for him easily.

Start by installing the class-transformer and class-validator packages for dto validation (see more in the docs), sharp for image optimization, and the S3 client packages.

The description of the request body model in NestJS is similar to the description of Ditsmod models, but NestJS uses decorators provided by the utility class-validator. In NestJS, it is customary to name the request body model with the ending *Dto (this is an abbreviation of Data transfer object):

You could use Zod or Superstruct to validate the types of values, or use typestack/class-validator if it makes sense.

Also, title of before article was I made 1,000x faster TypeScript validator library, but today's benchmark is showing that typescript-json is maximum 6,500x times faster. It's just because I added a new library class-validator in the benchmark and it is much slower than previous zod. Of course, I've continuously tuned the performance, but it just improved the performance about 2x times only.

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.