class-validator VS FluentValidation

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.

Here is an example of Command handler with built-in Fluent Validation and fire and forget style:

There is an RFC called Problem Details (RFC7807) that standardizes how an error in an API should be responded to for the client. If you use Fluent validation, you may have noticed that the response is within this pattern.

here we're defining the input validation requirements using fluent validation rules. let's see what happens when the user input doesn't meet the above criteria. execute the same request in swagger with the following incorrect json content:

I remember FluentValidation being a nice library for this type of stuff.

Literally the code example in the front page: https://fluentvalidation.net/

https://www.nuget.org/packages/Newtonsoft.Json/ https://www.nuget.org/packages/AutoMapper/ https://www.nuget.org/packages/Dapper/ https://www.nuget.org/packages/FluentValidation/ https://www.nuget.org/packages/FluentAssertions/ https://www.nuget.org/packages/NUnit/ https://www.nuget.org/packages/xunit/ https://www.nuget.org/packages/YamlDotNet/ https://www.nuget.org/packages/Moq/ That is simply not true. Mature c# projects purposely maintain no downstream dependencies and is they do, it's to a major reputable lib. See for yourself - these are staple third party packages commonly used. Anything dependency starting with System or NETStandard is Microsoft maintained.

FluentValidator for validating and authorising certain fields.

Now that we know the importance of providing users with enough information about their requests and how it was processed, let’s focus on how we can configure our API to help us do that.One aspect of such meaningful experience is Request Validation (400 Error Range). For that purpose I am using a library called Fluent Validation.