jet-schema

Jet-Schema ✈️

Simple, zero-dependency, typescript-first schema validation tool, that lets you integrate your own validator-functions.

Table of contents

• Introduction
• Quick Glance
• Guide
  • Getting Started
  • Creating Custom Schemas
  • Child schemas
  • Making schemas optional/nullable
  • Transforming values with "transform"
  • Combining Schemas
  • TypeScript Caveats
  • Bonus Features
• miscellaneous Notes
  • Creating wrapper functions
  • Recommended Defaults
    

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 an object. 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.

If you're open to jet-schema but think writing your own validator functions could be a hassle, you can copy-n-paste the file (https://github.com/seanpmaxwell/ts-validators/blob/master/src/validators.ts) into your application and add/remove/edit validators as needed.

Reasons to use Jet-Schema 😎

• Use your own validator-functions with schema-validation (this is why I wrote it).
• TypeScript first!
• Quick, terse, simple, easy-to-use (you can probably learn every feature in like 30 minutes).
• 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, test, parse functions provided automatically on every new schema.
• Provides a transform wrapper function to modify values before validating them.
• Default values can be set globally on initial setup or with the setDefault wrapper.
• Works client-side or server-side.
• Enums can be used for validation.
• Date constructor can be used to automatically transform and validate any valid date value.
• Doesn't require a compilation step (so still works with ts-node, unlike typia).
  

Quick Glance

import { setDefault, transform } from 'jet-schema';
import schema from 'utils/schema';
import { isRelKey, isString, isNumber, isOptionalStr } from 'utils/validators';

interface IUser {
  id: number;
  name: string;
  email: string;
  age: number;
  created: Date;
  address?: {
    street: string;
    zip: number;
    country?: string;
  };
}

const User = schema<IUser>({
  id: isRelKey,
  name: isString,
  email: setDefault(isEmail, '[e-mail]'),
  age: transform(Number, isNumber),
  created: Date,
  address: schema<IUser['address']>({
    street: isString,
    zip: isNumber,
    country: isOptionalStr,
  }, { optional: 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 an optional settings object with 3 three options:

• defaultValuesMap: An [["val", "function"]]nested array specifying 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. Upon initialization, the validator-functions will check their defaults. If a value is not optional and you do not supply a default value, then an error will be thrown when the schema is initialized. If you don't set a default value for a validator-function in jetSchema(), you can also call setDefault when setting up the schema (the next 3 snippets below contain examples).
• cloneFn: A custom clone-function if you don't want to use the built-in function which uses structuredClone (I like to use lodash.cloneDeep).
• onError: If a validator-function fails then an error is thrown. You can override this behavior by passing a custom error handling function as the third argument. This feature is really useful for testing when you may want to return an error string instead of throw an error.
  • Format: (property: string, value?: unknown, origMessage?: string, schemaId?: string) => void;.

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"
// As mentioned in the intro, you can copy some validators from here (https://github.com/seanpmaxwell/ts-validators/blob/master/src/validators.ts)
export const isStr = (arg: unknown): arg is string => typeof param === 'string';
export const isOptStr = (arg: unknown): arg is string => arg === undefined || typeof param === 'string';
export const isNum = (arg: unknown): arg is string => typeof param === 'number';

⚠️ 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 jetLogger({
  defaultValuesMap: [
    [isNum, 0],
    [isStr, ''],
  ],
  cloneFn: // pass a custom clone-function here
  onError: // pass a custom error-handler here,
});

Creating custom schemas

Now that we have our schema function setup, let's make a schema. Simply import the schema function from util/schema.ts and then your existing validator-functions and pass them as the value to each property in the schema function. If you want to set a default value at the schema level instead of at the global level in the jetSchema function, you can import the setDefault function from jet-schema and pass it the validator-function and the default value to go with it. Note that setting a default does not modify the validator function in any way. If a property is required then a default must be set for it (either globally or in setDefault) or else new won't know what to use as a value if the partial doesn't contain it.

For handling the overall schema's type, 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 an example of doing it both ways.

Personally, I like to create an interface first cause I feel like interfaces are great way to document your data-types; however, I created inferType because I know some people prefer to setup their schemas first and infer their types from that.

// "models/User.ts"
import { inferType, setDefault } from 'jet-schema';
import schema from 'util/schema.ts';
import { isNum, isStr, isOptionalStr } from 'util/validators.ts';

// **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: setDefault(isEmail, ''),
  nickName: isOptionalStr,
})

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

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

• new allows you to create new instances of your type using partials. If the property is absent, new will use the default supplied. If no default is supplied and the property is optional, then the value will be skipped. Runtime validation will still be done on every incoming property. Also, if you pass no parameter then a new instance will be created using all the defaults.
• test accepts any unknown value, tests that it's valid, and returns a type-predicate.
• pick allows you to select any property and returns an object with the test and default functions.
• parse is like a combination of new and test. It accepts an unknown value which is not optional, validates the properties but returns a new instance (while removing an extra ones) instead of a type-predicate. If you have an incoming unknown value (i.e. an api call) and you want to validate the properties and return a new instance while removing any possible extra ones, use parse. Note: only objects will pass the parse function, even if a schema is nullish, null/undefined values will not pass.

Child (aka nested) schemas

• 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, parse 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.
• There is one extra function schema() that you can call when using pick on a child-schema. This can be handy if you need to export a child-schema from one parent-schema to another:

interface IUserAlt {
  id: number;
  address: IUser['address'];
}

const UserAlt = schema<IUserAlt>({
  id: isNumber,
  address: User.pick('address').schema(),
});

Making schemas optional/nullable

In addition to a schema-object, the schema function accepts an additional options object parameter. The values here are type-checked against the generic (schema<"The Generic">(...)) that was passed so you must use the correct values. If your generic is optional/nullable then your are required to pass the object so at runtime the correct values are parsed.

{
  optional?: boolean; // default "false", must be true if generic is optional
  nullable?: boolean; // default "false", must be true if generic is nullable
  init?: boolean | null; // default "true", must be undefined, true, or null if generic is not optional.
  nullish?: true; // Use this instead of "{ optional: true, nullable: true; }"
  id?: string; // Will identify the schema in error messages
}

The option init defines the behavior when a schema is a child-schema and is being initialized from the parent. If true (default), then a nested child-object will be added to the property when a new instance of the parent is created. However, if a child-schema is optional or nullable, maybe you don't want a nested object and just want it to be null or skipped entirely. If init is null then nullable must be true, if false then optional must be true.

In the real world it's very common to have a lot of schemas which are both optional, nullable. So you don't have to write out { optional: true, nullable: true } over-and-over again, you can write { nullish: true } as an shorthand alternative.

You can also set the optional id field, if you need a unique identifier for your schema for whatever reason. If you set this option then it be added to the default error message. This can be useful if you have to debug a bunch of schemas at once (that's pretty much all I use it for).

Here's an example of the options in use:

// models/User.ts

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

const User = schema<IUser>({
  id: isNumber,
  name: isString,
  address: schema<IUser['address']>({
    street: isString,
    zip: isNumber,
  }, { nullish: true, init: false, id: 'User_address' }),
})

User.new() // => { id: 0, name: '' }

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 accepts a transforming-function and a validator-function and returns a new validator-function (type-predicate is preserved) which will transform the value before testing it. When calling new, test, or parse, transform will modify the original object.

If you want to access the transformed value yourself for whatever reason, you can pass a callback as the second argument to the returned validator-function and transform will supply the modified value to it. I've found transform can be useful for other parts of my application where I need to modify a value before validating it and then access the transformed value.

import { transform } from 'jet-schema';

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

Combining Schemas

For whatever reason, your schema may end up existing in multiple places. If you want to declare part of a schema that will be used elsewhere, 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 PartOfASchema: TJetSchema<{ id: number, name: string }> = {
  id: isNumber,
  name: isString,
} as const;

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

console.log(FullSchema.new());

TypeScript Caveats

Due to how structural-typing works in typescript, there are some limitations with typesafety that you need to be aware of. To put things in perspective, if type A has all the properties of type B, we can use type A for places where type B is required, even if A has additional properties.

Validator functions
If an object property's type can be string | undefined, then a validator-function whose type-predicate only returns arg is string will still work. However a if a type predicate returns arg is string | undefined we cannot use it for type string. This could cause runtime issues if a you pass a validator function like isString (when you should have passed isOptionalString) to a property whose value ends up being undefined.

interface IUser {
  id: string;
  name?: string;
}

const User = schema<IUser>({
  id: isString, // "isOptionalString" will throw type errors
  name: isOptionalString, // "isString" will not throw type errors but will throw runtime errors
})

Child schemas
As mentioned, if a property in a parent is mapped-object type (it as a defined set of keys), then you need to call schema again for the nested object. If you don't use a generic on the child-schema, typescript will still make sure all the required properties are there; however, because of structural-typing the child could have additional properties. It is highly-recommended that you pass a generic to your child-objects so additional properties don't get added.

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

const User = schema<IUser>({
  id: isNumber,
  address: schema<IUser['address']>({
    street: isString,
    // foo: isString, // If we left off the generic <IUser['address']> we could add "foo"
  }, { nullish: true }),
});

If you know of a way to enforce typesafety on child-object without requiring a generic please make a pull-request because I couldn't figure out a way.

Bonus Features

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

Miscellaneous Notes

Creating wrapper functions

If you need to modify the value of the test function for a property, (like removing nullables) then I recommended merging your schema with a new object and adding a wrapper function around that property's test function.

// models/User.ts
import { nonNullable } from 'util/validators.ts';

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

const User = schema<IUser>({
  id: isNumber,
  address: schema<IUser['address']>({
    street: isString,
    zip: isNumber,
  }, { nullish: true }),
})

export default {
  // Wrapper function to remove nullables
  checkAddr: nonNullable(User.pick('address').test),
  ...User,
}

Recommended Defaults

When calling the jetSchema function for this first time, at the very least, I highly recommend you set these default values for each of your basic primitive validator functions, unless of course your application has some other specific need.

// util/schema.ts
import { isNum, isStr, isBool } from 'util/validators.ts';

export default jetLogger({
  defaultValuesMap: [
    [isNum, 0],
    [isStr, ''],
    [isBool, false],
  ],
});