ajv VS joi

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!

Code in Docusaurus This module validates and processes frontmatter objects by using Joi library for the data validation.

Once you've leaped into thinking as a serverless problem solver, it makes a lot of sense to standardize some tooling and frameworks. The old adage right tool for the job is what I like about this layer of the pyramid. For instance, if a problem requires flexibility in data and your team enjoys validating schema with Joi then you might use TypeScript with a Node.js runtime with your lambdas. If you prefer the developer experience and small footprint and simplicity of Go then use the Go 1.x runtime. You might find you don't need "compute" at all, so using intrinsic functions in State Machines might be plenty.

Validation: request data validation using Joi

I'm using Joi on my server to validate form input, do you think I still need honeypot fields? It sounds like it might be worth looking into IP rate limiting as well, I don't think Joi can help with that.

What does your library provide that others don't? For example: https://github.com/colinhacks/zodhttps://github.com/hapijs/joihttps://github.com/jquense/yuphttps://github.com/gcanti/io-tshttps://github.com/pelotom/runtypeshttps://github.com/sindresorhus/ow

Yup's API is heavily inspired by Joi, but leaner and built with client-side validation as its primary use-case. Yup separates the parsing and validating functions into separate steps. cast() transforms data while validate checks that the input is the correct shape. Each can be performed together (such as HTML form validation) or seperately (such as deserializing trusted data from APIs).

Yup is a Javascript object schema validator: it lets you define a schema to describe how a valid object should look like, and allows you to validate an object using this schema. If you know Joi, Yup is heavily inspired by it, except it relies on client-side validation as its primary use-case.

Again a widely used open source validation library like Joi can help you easily create schemas and transform the data into safe objects.