jet-schema

Jet-Schema ✈️

Simple, zero-dependency, typescript-first schema validation tool, that lets you use your own validation functions (inferring types included!).

Table of contents

• Introduction
• Quick Glance
• Guide
  • Getting Started
  • Making schemas optional/nullable
  • Transforming values with transform()
  • Using Partial Schemas
  • Bonus Features

Introduction

Most schema validation libraries have fancy functions for validating objects and their properties, but the problem is I usually already have a lot of my own custom validation logic specific for each of my applications (i.e. functions to check primitive-types, regexes for validating strings etc). The only thing that was making me use schema-validation libraries was trying to validate object properties. So I thought, why not figure out a way to integrate my all the functions I had already written with something that can validate them against object properties? Well jet-schema does just that :)

If you want a library that includes all kinds of special functions for validating things other than objects, jet-schema is probably not for you. However, the vast majority of projects I've worked on have involved implementing lots of type-checking functions specific to the needs of that project. For example, maybe the email format that's built into the library is different than the one your application needs. Instead of of having to dig into the library's features to validate using your custom method, with jet-schema you can just pass your method.

Reasons to use Jet-Schema 😎

• TypeScript first!
• Quick, terse, simple, easy-to-use (there are only 3 function exports and 2 type exports).
• Much smaller and less complex than most schema-validation libraries.
• Typesafety works both ways, you can either force a schema structure using a pre-defined type OR you can infer a type from a schema.
• new and test functions provided automatically on every new schema.
• Provides a transform wrapper function to modify values after before validating them.
• Works client-side or server-side.
• Enums can be used for validation.
• Doesn't require a compilation step (so still works with ts-node, unlike typia).
  

Quick Glance

// An example using "zod", a popular schema validation library
const User: z.ZodType<IUser> = z.object({
  id: z.number().default(-1).min(-1),
  name: z.string().default(''),
  email: z.string().email().or(z.literal('')).default('[e-mail]'),
  age: z.preprocess(Number, z.number()),
  created: z.preprocess((arg => arg === undefined ? new Date() : arg), z.coerce.date()),
  address: z.object({ 
    street: z.string(),
    zip: z.number(),
    country: z.string().optional(),
  }).optional(),
});

// Equivalent using "jet-schema" (other than "schema/transform", the other functions 
// are defined elsewhere in the application)
const User = schema<IUser>({
  id: isRelKey,
  name: isString,
  email: ['[e-mail]', isEmail],
  age: transform(Number, isNumber),
  created: Date,
  address: schema({
    street: isString,
    zip: isNumber,
    country: isOptionalStr,
  }, true),
});

Guide

Getting Started

npm install -s jet-schema

After installation, you need to configure the schema function by importing and calling the jetSchema() function.

jetSchema() accepts two optional arguments:

• An array-map of which default-value should be used for which validator-function: you should use this option for frequently used validator-function/default-value combinations where you don't want to set a default value every time.
• The second is a custom clone function if you don't want to use the built-in function which uses structuredClone (I like to use lodash.cloneDeep).
  

When setting up jet-schema for the first time, usually what I do is create two files under my util/ folder: schema.ts and validators.ts. In schema.ts I'll import and call the jet-schema function then apply any frequently used validator-function/default-value combinations I have and a clone-function. If you don't want to go through this step, you can import the schema function directly from jet-schema.

// "util/validators.ts"

export function isNum(param: unknown): param is number {
  return typeof param === 'number';
}

export function isStr(param: unknown): param is string {
  return typeof param === 'string';
}

export function isOptionalStr(param: unknown): param is string | undefined {
  return param === undefined || typeof param === 'string';
}

⚠️ IMPORTANT:   You need to use type-predicates when writing validator functions. If a value can be null/undefined, your validator-function's type-predicate needs account for this (i.e. (arg: unknown): arg is string | undefined => ... ).

// "util/schema.ts"
import jetSchema from 'jet-schema';
import { isNum, isStr } from './validators';

export default jetSchema([
  [isNum, 0],
  [isStr, ''],
], '...pass a custom clone-function here if you want to...');

Now that we have our schema function setup, let's make a schema: there are two ways to go about this, enforcing a schema from a type or infering a type from a schema. I'll show you some examples doing it both ways.

Personally, I like to create an interface first cause I feel like they are great way to document your data-types, but I know some people prefer to setup their schemas first and infer their types from that.

// "models/User.ts"
import { inferType } from 'jet-schema';

import schema from 'util/schema.ts';
import { isNum, isStr, isOptionalStr } from 'util/type-checks';


// **OPTION 1**: Create schema using type
interface IUser {
  id: number;
  name: string;
  email: string;
  nickName?: string; 
}
const User = schema<IUser>({
  id: isNum,
  name: isStr,
  email: ['', EMAIL_RGX.test] // You can pass your defaults/validators in an array instead.
  nickName: isOptionalStr, // NOTE: we don't have to pass this option since its optional
})

// **OPTION 2**: Create type using schema
const User = schema({
  id: isNum,
  name: isStr,
  email: ['', EMAIL_RGX.test]
  nickName: isOptionalStr,
})
const TUser = inferType<typeof User>;

⚠️ IMPORTANT:   Upon initialization, the validator-functions will check their defaults. If a value is not optional and you do not supply default value, then an error will be thrown when schema() is called.

Once you have your schema setup, you can call the new, test, and pick functions. Here is an overview of what each one does:

• new Allows you to create new instances of your type using partials. If the value is absent, new will using the default supplied. If no default is supplied then the value will be skipped.
• test accepts any unknown value, returns a type-predicate and will test it against the schema.
• pick allows you to select property and returns an object with the test and default functions. If a value is nullable, then you need to use optional guard when calling it: pick?.()

If an object property is a mapped-type then it must be initialized with the schema function. Just like with the parent schemas, you can also call new, test, pick, in addition to default. The value returned from default could be different from new if the schema is optional/nullable and the default value is null or undefined.

Making schemas optional/nullable

In addition to a schema-object, the schema() function accepts 3 additional parameters isOptional, isNullable, and default. These are type-checked against the type supplied to schema schema<...Your Type...>(), so you must supply the correct parameters. So for example, if the schema-type is nullable and optional, then you must enter true for the second and third parameters.

The third option default defines the behavior for nested schemas when initialized from a parent. The value can be a boolean or null. If false the value will not be initialized with the parent, if null (the schema must be nullable to do this) value will be null, and if true or undefined then a full schema object will be created when a parent object is created.

interface IUser {
  id: number;
  name: string;
  address?: { street: string, zip: number } | null;
}

const User = schema<IUser>({
  id: isNumber,
  name: isString,
  address: schema({
    street: isString,
    zip: isNumber,
  }, true /*(isOptional)*/, true /*(isNullable)*/, /*default*/) // You MUST pass true for "isOptional" and "isNullable" here.
})

Transforming values with transform()

If you want to modify a value before it passes through a validator function, you can import the transform function and wrap your validator function with it. transform calls the validator function and fires a callback with the modified value if the callback was provided. When calling new or test, transform will modify the original object.

I've found transform can be useful for other parts of my application where I need to modify a value before validating it and return the transformed value. The function firing the callback still returns the validator's type-predicate:

import { transform } from 'jet-schema';

const customTest = transform(JSON.parse, isNumberArray);
let val = '[1,2,3,5]';
console.log(customTest(val, transVal => val = transVal)); // => true
console.log(val); // => [1,2,3,5] this is number array not a string

Using Partial Schemas

For whatever reason, your schema may end up existing in multiple places. If you want to declare a partial schema, you can import the TJetSchema type and use it to setup one, then merge it with your full schema later.

import schema, { TJetSchema } from 'jet-schema';

const PartialSchema: TJetSchema<{ id: number, name: string }> = {
  id: isNumber,
  name: isString,
} as const;

const FullSchema = schema<{ id: number, name: string, e: boolean }>({
  ...PartialSchema,
  e: isBoolean,
});

console.log(FullSchema.new());

Bonus Features

• When passing the Date constructor, jet-schema automatically converts all valid date values (i.e. string/number ) to a Date object. The default value will be a current datetime Date object.
• You can also use an enum as a validator. The default value will be the first value in the enum object and the validation will make sure the value is a value in the enum.