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@effect/schema

Modeling the schema of data structures as first-class values

Source

npm

Version: 0.54.1

Version published: 3 years ago

Weekly downloads: 0

Maintainers: 3

Weekly downloads

Created: 4 years ago

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Modeling the schema of data structures as first-class values

Introduction

Welcome to the documentation for @effect/schema, a library for defining and using schemas to validate and transform data in TypeScript.

@effect/schema allows you to define a Schema<I, A> that provides a blueprint for describing the structure and data types of your data. Once defined, you can leverage this schema to perform a range of operations, including:

Operation	Description
Parsing	Convert from `unknown` value to output type `A`.
Decoding	Transforming data from an input type `I` to an output type `A`.
Encoding	Converting data from an output type `A` back to an input type `I`.
Asserting	Verifying that a value adheres to the schema's output type `A`.
Arbitraries	Generate arbitraries for fast-check testing.
Pretty printing	Support pretty printing for data structures.
JSON Schemas	Create JSON Schemas based on defined schemas.
Equivalence	Create Equivalences based on defined schemas.

If you're eager to learn how to define your first schema, jump straight to the Basic usage section!

Understanding Parsing, Decoding, and Encoding

We'll break down these concepts using an example with a Schema<string, Date>. This schema serves as a tool to transform a string into a Date and vice versa.

Encoding

When we talk about "encoding," we are referring to the process of changing a Date into a string. To put it simply, it's the act of converting data from one format to another.

Decoding

Conversely, "decoding" entails transforming a string back into a Date. It's essentially the reverse operation of encoding, where data is returned to its original form.

Parsing

Parsing involves two key steps:

Checking: Initially, we verify that the input data (which is of the unknown type) matches the expected structure. In our specific case, this means ensuring that the input is indeed a string.
Decoding: Following the successful check, we proceed to convert the string into a Date. This process completes the parsing operation, where the data is both validated and transformed.

[!NOTE] As a general rule, schemas should be defined such that encode + decode return the original value.

The Rule of Schemas: Keeping Encode and Decode in Sync

When working with schemas, there's an important rule to keep in mind: your schemas should be crafted in a way that when you perform both encoding and decoding operations, you should end up with the original value.

In simpler terms, if you encode a value and then immediately decode it, the result should match the original value you started with. This rule ensures that your data remains consistent and reliable throughout the encoding and decoding process.

Credits

This library was inspired by the following projects:

Requirements

TypeScript 5.0 or newer
The strict flag enabled in your tsconfig.json file
The exactOptionalPropertyTypes flag enabled in your tsconfig.json file
```
{
  // ...
  "compilerOptions": {
    // ...
    "strict": true,
    "exactOptionalPropertyTypes": true
  }
}
```
Additionally, make sure to install the following packages, as they are peer dependencies. Note that some package managers might not install peer dependencies by default, so you need to install them manually:
- effect package (peer dependency)
- fast-check package (peer dependency)

Understanding `exactOptionalPropertyTypes`

The @effect/schema library takes advantage of the exactOptionalPropertyTypes option of tsconfig.json. This option affects how optional properties are typed (to learn more about this option, you can refer to the official TypeScript documentation).

Let's delve into this with an example.

With exactOptionalPropertyTypes Enabled

import * as Schema from "@effect/schema/Schema";

/*
const schema: Schema.Schema<{
    readonly myfield?: string; // the type is strict
}, {
    readonly myfield?: string; // the type is strict
}>
*/
const schema = Schema.struct({
  myfield: Schema.optional(Schema.string.pipe(Schema.nonEmpty())),
});

Schema.decodeSync(schema)({ myfield: undefined }); // Error: Type 'undefined' is not assignable to type 'string'.ts(2379)

Here, notice that the type of myfield is strict (string), which means the type checker will catch any attempt to assign an invalid value (like undefined).

With exactOptionalPropertyTypes Disabled

If, for some reason, you can't enable the exactOptionalPropertyTypes option (perhaps due to conflicts with other third-party libraries), you can still use @effect/schema. However, there will be a mismatch between the types and the runtime behavior:

import * as Schema from "@effect/schema/Schema";

/*
const schema: Schema.Schema<{
    readonly myfield?: string | undefined; // the type is widened to string | undefined
}, {
    readonly myfield?: string | undefined; // the type is widened to string | undefined
}>
*/
const schema = Schema.struct({
  myfield: Schema.optional(Schema.string.pipe(Schema.nonEmpty())),
});

Schema.decodeSync(schema)({ myfield: undefined }); // No type error, but a decoding failure occurs
/*
Error: error(s) found
└─ ["a"]
   └─ Expected string, actual undefined
*/

In this case, the type of myfield is widened to string | undefined, which means the type checker won't catch the invalid value (undefined). However, during decoding, you'll encounter an error, indicating that undefined is not allowed.

Getting started

To install the alpha version:

npm install @effect/schema

Additionally, make sure to install the following packages, as they are peer dependencies. Note that some package managers might not install peer dependencies by default, so you need to install them manually:

effect package (peer dependency)
fast-check package (peer dependency)

[!WARNING] This package is primarily published to receive early feedback and for contributors, during this development phase we cannot guarantee the stability of the APIs, consider each release to contain breaking changes.

Once you have installed the library, you can import the necessary types and functions from the @effect/schema/Schema module.

import * as S from "@effect/schema/Schema";

Defining a schema

To define a Schema, you can use the provided struct function to define a new Schema that describes an object with a fixed set of properties. Each property of the object is described by a Schema, which specifies the data type and validation rules for that property.

For example, consider the following Schema that describes a person object with a name property of type string and an age property of type number:

import * as S from "@effect/schema/Schema";

const Person = S.struct({
  name: S.string,
  age: S.number,
});

You can also use the union function to define a Schema that describes a value that can be one of a fixed set of types. For example, the following Schema describes a value that can be either a string or a number:

const StringOrNumber = S.union(S.string, S.number);

In addition to the provided struct and union functions, @effect/schema/Schema also provides a number of other functions for defining Schemas, including functions for defining arrays, tuples, and records.

Extracting Inferred Types

After you've defined a Schema<I, A>, you can extract the inferred type A that represents the data described by the schema using the Schema.To type.

For instance, with the Person schema we defined earlier, you can extract the inferred type of a Person object as demonstrated below:

import * as S from "@effect/schema/Schema";

const Person = S.struct({
  name: S.string,
  age: S.number,
});

interface Person extends S.Schema.To<typeof Person> {}
/*
Equivalent to:
interface Person {
  readonly name: string;
  readonly age: number;
}
*/

In this example, we use interface instead of type:

type Person = S.Schema.To<typeof Person>;

to create an opaque type.

In cases where I differs from A, you can also extract the inferred I type using Schema.From.

To create a schema with an opaque type, you can use the following technique that re-declares the schema:

import * as S from "@effect/schema/Schema";

const _Person = S.struct({
  name: S.string,
  age: S.number,
});

interface Person extends S.Schema.To<typeof Person> {}

// Re-declare the schema to create a schema with an opaque type
const Person: Schema.Schema<Person> = Person_;

Alternatively, you can use Schema.Class (see the Class section below for more details).

Parsing

To parse a value from the unknown type using the previously defined Schema, you can make use of the parse* functions provided by the @effect/schema/Schema module. Let's explore an example using the parseEither function:

import * as S from "@effect/schema/Schema";
import * as Either from "effect/Either";

const Person = S.struct({
  name: S.string,
  age: S.number,
});

const parsePerson = S.parseEither(Person);

const input: unknown = { name: "Alice", age: 30 };

const result1 = parsePerson(input);
if (Either.isRight(result1)) {
  console.log(result1.right);
  /*
  Output:
  { name: "Alice", age: 30 }
  */
}

const result2 = parsePerson(null);
if (Either.isLeft(result2)) {
  console.log(result2.left);
  /*
  Output:
  {
    _id: 'ParseError',
    message: 'error(s) found\n└─ Expected <anonymous type literal schema>, actual null'
  }
  */
}

The parsePerson function returns an Either<ParseError, A>, where ParseError is defined as follows:

interface ParseError {
  readonly _tag: "ParseError";
  // A non-empty list of errors
  readonly errors: ReadonlyArray.NonEmptyReadonlyArray<ParseIssue>;
}

ParseError represents a list of errors that may have occurred during the parsing process, and A is the inferred data type described by the Schema. A successful parse results in a Right value, containing the parsed data A. In the case of a failed parse, the result will be a Left value containing a non-empty list of ParseIssue.

Now, let's see another example using the parseSync function.

The parseSync function is used to parse a value and throw an error if the parsing fails. This is especially useful when you want to ensure that the parsed value adheres to the correct format and are ready to throw an error if it does not.

try {
  const person = S.parseSync(Person)({});
  console.log(person);
} catch (e) {
  console.error("Parsing failed:");
  console.error(e);
}
/*
Parsing failed:
Error: error(s) found
└─ ["name"]
   └─ is missing
  ...stack...
*/

In this example, we attempt to parse an empty object, but the name property is missing, resulting in an error being thrown.

Handling Async Transformations

When your schema involves asynchronous transformations, neither the parseSync nor the parseEither functions will work for you. In such cases, you must turn to the parse function, which returns an Effect.

import * as S from "@effect/schema/Schema";
import { Effect } from "effect";

const PersonId = S.number;

const Person = S.struct({
  id: PersonId,
  name: S.string,
  age: S.number,
});

const asyncSchema = S.transformOrFail(
  PersonId,
  Person,
  // Simulate an async transformation
  (id) =>
    Effect.succeed({ id, name: "name", age: 18 }).pipe(
      Effect.delay("10 millis")
    ),
  (person) => Effect.succeed(person.id).pipe(Effect.delay("10 millis"))
);

const syncParsePersonId = S.parseEither(asyncSchema);

console.log(JSON.stringify(syncParsePersonId(1), null, 2));
/*
Output:
{
  "_id": "Either",
  "_tag": "Left",
  "left": {
    "_id": "ParseError",
    "message": "error(s) found\n└─ is forbidden"
  }
}
*/

const asyncParsePersonId = S.parse(asyncSchema);

Effect.runPromise(asyncParsePersonId(1)).then(console.log);
/*
Output:
{ id: 1, name: 'name', age: 18 }
*/

As shown in the code above, the first approach returns a Forbidden error, indicating that using parseEither with an async transformation is not allowed. However, the second approach works as expected, allowing you to handle async transformations and return the desired result.

Excess properties

When using a Schema to parse a value, any properties that are not specified in the Schema will be stripped out from the output. This is because the Schema is expecting a specific shape for the parsed value, and any excess properties do not conform to that shape.

However, you can use the onExcessProperty option (default value: "ignore") to trigger a parsing error. This can be particularly useful in cases where you need to detect and handle potential errors or unexpected values.

Here's an example of how you might use onExcessProperty:

import * as S from "@effect/schema/Schema";

const Person = S.struct({
  name: S.string,
  age: S.number,
});

console.log(
  S.parseSync(Person)({
    name: "Bob",
    age: 40,
    email: "bob@example.com",
  })
);
/*
Output:
{ name: 'Bob', age: 40 }
*/

S.parseSync(Person)(
  {
    name: "Bob",
    age: 40,
    email: "bob@example.com",
  },
  { onExcessProperty: "error" }
);
/*
throws
Error: error(s) found
└─ ["email"]
   └─ is unexpected, expected "name" or "age"
*/

All errors

The errors option allows you to receive all parsing errors when attempting to parse a value using a schema. By default only the first error is returned, but by setting the errors option to "all", you can receive all errors that occurred during the parsing process. This can be useful for debugging or for providing more comprehensive error messages to the user.

Here's an example of how you might use errors:

import * as S from "@effect/schema/Schema";

const Person = S.struct({
  name: S.string,
  age: S.number,
});

S.parseSync(Person)(
  {
    name: "Bob",
    age: "abc",
    email: "bob@example.com",
  },
  { errors: "all", onExcessProperty: "error" }
);
/*
throws
Error: error(s) found
├─ ["email"]
│  └─ is unexpected, expected "name" or "age"
└─ ["age"]
   └─ Expected number, actual "abc"
*/

Encoding

To use the Schema defined above to encode a value to unknown, you can use the encode function:

import * as S from "@effect/schema/Schema";
import * as Either from "effect/Either";

// Age is a schema that can decode a string to a number and encode a number to a string
const Age = S.NumberFromString;

const Person = S.struct({
  name: S.string,
  age: Age,
});

const encoded = S.encodeEither(Person)({ name: "Alice", age: 30 });
if (Either.isRight(encoded)) {
  console.log(encoded.right);
  /*
  Output:
  { name: "Alice", age: "30" }
  */
}

Note that during encoding, the number value 30 was converted to a string "30".

Formatting Errors

When you're working with Effect Schema and encounter errors during parsing, decoding, or encoding functions, you can format these errors in two different ways: using the TreeFormatter or the ArrayFormatter.

TreeFormatter (default)

The TreeFormatter is the default way to format errors. It arranges errors in a tree structure, making it easy to see the hierarchy of issues.

Here's an example of how it works:

import * as S from "@effect/schema/Schema";
import { formatErrors } from "@effect/schema/TreeFormatter";
import * as Either from "effect/Either";

const Person = S.struct({
  name: S.string,
  age: S.number,
});

const result = S.parseEither(Person)({});
if (Either.isLeft(result)) {
  console.error("Parsing failed:");
  console.error(formatErrors(result.left.errors));
}
/*
Parsing failed:
error(s) found
└─ ["name"]
   └─ is missing
*/

ArrayFormatter

The ArrayFormatter is an alternative way to format errors, presenting them as an array of issues. Each issue contains properties such as _tag, path, and message:

interface Issue {
  readonly _tag: ParseErrors["_tag"];
  readonly path: ReadonlyArray<PropertyKey>;
  readonly message: string;
}

Here's an example of how it works:

import { formatErrors } from "@effect/schema/ArrayFormatter";
import * as S from "@effect/schema/Schema";
import * as Either from "effect/Either";

const Person = S.struct({
  name: S.string,
  age: S.number,
});

const result = S.parseEither(Person)(
  { name: 1, foo: 2 },
  { errors: "all", onExcessProperty: "error" }
);
if (Either.isLeft(result)) {
  console.error("Parsing failed:");
  console.error(formatErrors(result.left.errors));
}
/*
Parsing failed:
[
  {
    _tag: 'Unexpected',
    path: [ 'foo' ],
    message: 'Unexpected, expected "name" or "age"'
  },
  {
    _tag: 'Type',
    path: [ 'name' ],
    message: 'Expected string, actual 1'
  },
  { _tag: 'Missing', path: [ 'age' ], message: 'Missing key or index' }
]
*/

Assertions

The is function provided by the @effect/schema/Schema module represents a way of verifying that a value conforms to a given Schema. is is a refinement that takes a value of type unknown as an argument and returns a boolean indicating whether or not the value conforms to the Schema.

import * as S from "@effect/schema/Schema";

const Person = S.struct({
  name: S.string,
  age: S.number,
});

// const isPerson: (u: unknown) => u is Person
const isPerson = S.is(Person);

console.log(isPerson({ name: "Alice", age: 30 })); // true
console.log(isPerson(null)); // false
console.log(isPerson({})); // false

The asserts function takes a Schema and returns a function that takes an input value and checks if it matches the schema. If it does not match the schema, it throws an error with a comprehensive error message.

import * as S from "@effect/schema/Schema";

const Person = S.struct({
  name: S.string,
  age: S.number,
});

// const assertsPerson: (input: unknown, options?: ParseOptions) => asserts input is { readonly name: string; readonly age: number; }
const assertsPerson: S.Schema.ToAsserts<typeof Person> = S.asserts(Person);

try {
  assertsPerson({ name: "Alice", age: "30" });
} catch (e) {
  console.error("The input does not match the schema:");
  console.error(e);
}
/*
The input does not match the schema:
Error: error(s) found
└─ ["age"]
   └─ Expected number, actual "30"
*/

// this will not throw an error
assertsPerson({ name: "Alice", age: 30 });

fast-check arbitraries

The arbitrary function provided by the @effect/schema/Arbitrary module represents a way of generating random values that conform to a given Schema. This can be useful for testing purposes, as it allows you to generate random test data that is guaranteed to be valid according to the Schema.

import * as Arbitrary from "@effect/schema/Arbitrary";
import * as S from "@effect/schema/Schema";
import * as fc from "fast-check";

const Person = S.struct({
  name: S.string,
  age: S.string.pipe(S.numberFromString, S.int()),
});

// Arbitrary for the To type
const PersonArbitraryTo = Arbitrary.to(Person)(fc);

console.log(fc.sample(PersonArbitraryTo, 2));
/*
Output:
[ { name: 'iP=!', age: -6 }, { name: '', age: 14 } ]
*/

// Arbitrary for the From type
const PersonArbitraryFrom = Arbitrary.from(Person)(fc);

console.log(fc.sample(PersonArbitraryFrom, 2));
/*
Output:
[ { name: '{F', age: '$"{|' }, { name: 'nB}@BK', age: '^V+|W!Z' } ]
*/

Pretty print

The to function provided by the @effect/schema/Pretty module represents a way of pretty-printing values that conform to a given Schema.

You can use the to function to create a human-readable string representation of a value that conforms to a Schema. This can be useful for debugging or logging purposes, as it allows you to easily inspect the structure and data types of the value.

import * as Pretty from "@effect/schema/Pretty";
import * as S from "@effect/schema/Schema";

const Person = S.struct({
  name: S.string,
  age: S.number,
});

const PersonPretty = Pretty.to(Person);

// returns a string representation of the object
console.log(PersonPretty({ name: "Alice", age: 30 }));
/*
Output:
{ "name": "Alice", "age": 30 }
*/

Generating JSON Schemas

The to / from functions, which are part of the @effect/schema/JSONSchema module, allow you to generate a JSON Schema based on a schema definition:

import * as JSONSchema from "@effect/schema/JSONSchema";
import * as S from "@effect/schema/Schema";

const Person = S.struct({
  name: S.string,
  age: S.number,
});

const jsonSchema = JSONSchema.to(Person);

console.log(JSON.stringify(jsonSchema, null, 2));
/*
Output:
{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "type": "object",
  "required": [
    "name",
    "age"
  ],
  "properties": {
    "name": {
      "type": "string",
      "description": "a string",
      "title": "string"
    },
    "age": {
      "type": "number",
      "description": "a number",
      "title": "number"
    }
  },
  "additionalProperties": false
}
*/

In this example, we have created a schema for a "Person" with a name (a string) and an age (a number). We then use the JSONSchema.to function to generate the corresponding JSON Schema.

Identifier Annotations

You can enhance your schemas with identifier annotations. If you do, your schema will be included within a "definitions" object property on the root and referenced from there:

import * as JSONSchema from "@effect/schema/JSONSchema";
import * as S from "@effect/schema/Schema";

const Name = S.string.pipe(S.identifier("Name"));
const Age = S.number.pipe(S.identifier("Age"));
const Person = S.struct({
  name: Name,
  age: Age,
});

const jsonSchema = JSONSchema.to(Person);

console.log(JSON.stringify(jsonSchema, null, 2));
/*
Output:
{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "type": "object",
  "required": [
    "name",
    "age"
  ],
  "properties": {
    "name": {
      "$ref": "#/$defs/Name"
    },
    "age": {
      "$ref": "#/$defs/Age"
    }
  },
  "additionalProperties": false,
  "$defs": {
    "Name": {
      "type": "string",
      "description": "a string",
      "title": "string"
    },
    "Age": {
      "type": "number",
      "description": "a number",
      "title": "number"
    }
  }
}
*/

This technique helps organize your JSON Schema by creating separate definitions for each identifier annotated schema, making it more readable and maintainable.

Recursive and Mutually Recursive Schemas

Recursive and mutually recursive schemas are supported, but in these cases, identifier annotations are required:

import * as JSONSchema from "@effect/schema/JSONSchema";
import * as S from "@effect/schema/Schema";

interface Category {
  readonly name: string;
  readonly categories: ReadonlyArray<Category>;
}

const schema: S.Schema<Category> = S.struct({
  name: S.string,
  categories: S.array(S.suspend(() => schema).pipe(S.identifier("Category"))),
});

const jsonSchema = JSONSchema.to(schema);

console.log(JSON.stringify(jsonSchema, null, 2));
/*
Output:
{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "$ref": "#/$defs/Category",
  "$defs": {
    "Category": {
      "type": "object",
      "required": [
        "name",
        "categories"
      ],
      "properties": {
        "name": {
          "type": "string",
          "description": "a string",
          "title": "string"
        },
        "categories": {
          "type": "array",
          "items": {
            "$ref": "#/$defs/Category"
          }
        }
      },
      "additionalProperties": false
    }
  }
}
*/

In the example above, we define a schema for a "Category" that can contain a "name" (a string) and an array of nested "categories." To support recursive definitions, we use the S.suspend function and identifier annotations to name our schema.

This ensures that the JSON Schema properly handles the recursive structure and creates distinct definitions for each annotated schema, improving readability and maintainability.

JSON Schema Annotations

When defining a refinement (e.g., through the filter function), you can attach a JSON Schema annotation to your schema containing a JSON Schema "fragment" related to this particular refinement. This fragment will be used to generate the corresponding JSON Schema. Note that if the schema consists of more than one refinement, the corresponding annotations will be merged.

import * as JSONSchema from "@effect/schema/JSONSchema";
import * as Schema from "@effect/schema/Schema";

// Simulate one or more refinements
const Positive = Schema.number.pipe(
  Schema.filter((n) => n > 0, {
    jsonSchema: { minimum: 0 },
  })
);

const schema = Positive.pipe(
  Schema.filter((n) => n <= 10, {
    jsonSchema: { maximum: 10 },
  })
);

console.log(JSONSchema.to(schema));
/*
Output:
{
  '$schema': 'http://json-schema.org/draft-07/schema#',
  type: 'number',
  description: 'a number',
  title: 'number',
  minimum: 0,
  maximum: 10
}
*/

As seen in the example, the JSON Schema annotations are merged with the base JSON Schema from Schema.number. This approach helps handle multiple refinements while maintaining clarity in your code.

Generating Equivalences

The to function, which is part of the @effect/schema/Equivalence module, allows you to generate an Equivalence based on a schema definition:

import * as S from "@effect/schema/Schema";
import * as Equivalence from "@effect/schema/Equivalence";

const Person = S.struct({
  name: S.string,
  age: S.number,
});

// $ExpectType Equivalence<{ readonly name: string; readonly age: number; }>
const PersonEquivalence = Equivalence.to(Person);

const john = { name: "John", age: 23 };
const alice = { name: "Alice", age: 30 };

console.log(PersonEquivalence(john, { name: "John", age: 23 })); // Output: true
console.log(PersonEquivalence(john, alice)); // Output: false

Basic usage

Cheatsheet

Typescript Type	Description / Notes	Schema / Combinator
`null`		`S.null`
`undefined`		`S.undefined`
`string`		`S.string`
`number`		`S.number`
`boolean`		`S.boolean`
`symbol`		`S.symbolFromSelf` / `S.symbol`
`bigint`		`S.bigintFromSelf` / `S.bigint`
`unknown`		`S.unknown`
`any`		`S.any`
`never`		`S.never`
`object`		`S.object`
`unique symbol`		`S.uniqueSymbol`
`"a"`, `1`, `true`	type literals	`S.literal("a")`, `S.literal(1)`, `S.literal(true)`
`a${string}`	template literals	`S.templateLiteral(S.literal("a"), S.string)`
`{ readonly a: string, readonly b: number }`	structs	`S.struct({ a: S.string, b: S.number })`
`{ readonly a?: string }`	optional fields	`S.struct({ a: S.optional(S.string) })`
`Record<A, B>`	records	`S.record(A, B)`
`readonly [string, number]`	tuples	`S.tuple(S.string, S.number)`
`ReadonlyArray<string>`	arrays	`S.array(S.string)`
`A \| B`	unions	`S.union(A, B)`
`A & B`	intersections of non-overlapping structs	`S.extend(A, B)`
`Record<A, B> & Record<C, D>`	intersections of non-overlapping records	`S.extend(S.record(A, B), S.record(C, D))`
`type A = { readonly a: A \| null }`	recursive types	`S.struct({ a: S.union(S.null, S.suspend(() => self)) })`
`keyof A`		`S.keyof(A)`
`Partial<A>`		`S.partial(A)`
`Required<A>`		`S.required(A)`

Primitives

import * as S from "@effect/schema/Schema";

// primitive values
S.string;
S.number;
S.bigint; // Schema<string, bigint>
S.boolean;
S.symbol; // Schema<string, symbol>
S.object;

// empty types
S.undefined;
S.void; // accepts undefined

// catch-all types
// allows any value
S.any;
S.unknown;

// never type
// allows no values
S.never;

S.json;
S.UUID;
S.ULID;

Literals

S.null; // same as S.literal(null)
S.literal("a");
S.literal("a", "b", "c"); // union of literals
S.literal(1);
S.literal(2n); // bigint literal
S.literal(true);

Template literals

The templateLiteral combinator allows you to create a schema for a TypeScript template literal type.

// $ExpectType Schema<`a${string}`>
S.templateLiteral(S.literal("a"), S.string);

// example from https://www.typescriptlang.org/docs/handbook/2/template-literal-types.html
const EmailLocaleIDs = S.literal("welcome_email", "email_heading");
const FooterLocaleIDs = S.literal("footer_title", "footer_sendoff");

// $ExpectType Schema<"welcome_email_id" | "email_heading_id" | "footer_title_id" | "footer_sendoff_id">
S.templateLiteral(S.union(EmailLocaleIDs, FooterLocaleIDs), S.literal("_id"));

Filters

In the @effect/schema/Schema library, you can apply custom validation logic using filters.

You can define a custom validation check on any schema using the filter function. Here's a simple example:

import * as S from "@effect/schema/Schema";

const LongString = S.string.pipe(
  S.filter((s) => s.length >= 10, {
    message: () => "a string at least 10 characters long",
  })
);

console.log(S.parseSync(LongString)("a"));
/*
throws:
error(s) found
└─ a string at least 10 characters long
*/

It's recommended to include as much metadata as possible for later introspection of the schema, such as an identifier, JSON schema representation, and a description:

const LongString = S.string.pipe(
  S.filter((s) => s.length >= 10, {
    message: () => "a string at least 10 characters long",
    identifier: "LongString",
    jsonSchema: { minLength: 10 },
    description:
      "Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua",
  })
);

For more complex scenarios, you can return an Option<ParseError> type instead of a boolean. In this context, None indicates success, and Some(error) rejects the input with a specific error. Here's an example:

import * as ParseResult from "@effect/schema/ParseResult";
import * as S from "@effect/schema/Schema";

const schema = S.struct({ a: S.string, b: S.string }).pipe(
  S.filter((o) =>
    o.b === o.a
      ? Option.none()
      : Option.some(
          ParseResult.parseError([
            ParseResult.key("b", [
              ParseResult.type(
                S.literal(o.a).ast,
                o.b,
                `should be equal to a's value ("${o.a}")`
              ),
            ]),
          ])
        )
  )
);

console.log(S.parseSync(schema)({ a: "a", b: "b" }));
/*
throws:
error(s) found
└─ ["b"]
   └─ should be equal to a's value ("a")
*/

[!WARNING] Please note that the use of filters do not alter the type of the Schema. They only serve to add additional constraints to the parsing process.

String filters

S.string.pipe(S.maxLength(5));
S.string.pipe(S.minLength(5));
S.string.pipe(nonEmpty()); // same as S.minLength(1)
S.string.pipe(S.length(5));
S.string.pipe(S.pattern(regex));
S.string.pipe(S.startsWith(string));
S.string.pipe(S.endsWith(string));
S.string.pipe(S.includes(searchString));
S.string.pipe(S.trimmed()); // verifies that a string contains no leading or trailing whitespaces
S.string.pipe(S.lowercased()); // verifies that a string is lowercased

Note: The trimmed combinator does not make any transformations, it only validates. If what you were looking for was a combinator to trim strings, then check out the trim combinator ot the Trim schema.

Number filters

S.number.pipe(S.greaterThan(5));
S.number.pipe(S.greaterThanOrEqualTo(5));
S.number.pipe(S.lessThan(5));
S.number.pipe(S.lessThanOrEqualTo(5));
S.number.pipe(S.between(-2, 2)); // -2 <= x <= 2

S.number.pipe(S.int()); // value must be an integer

S.number.pipe(S.nonNaN()); // not NaN
S.number.pipe(S.finite()); // ensures that the value being parsed is finite and not equal to Infinity or -Infinity

S.number.pipe(S.positive()); // > 0
S.number.pipe(S.nonNegative()); // >= 0
S.number.pipe(S.negative()); // < 0
S.number.pipe(S.nonPositive()); // <= 0

S.number.pipe(S.multipleOf(5)); // evenly divisible by 5

Bigint filters

import * as S from "@effect/schema/Schema";

S.bigint.pipe(S.greaterThanBigint(5n));
S.bigint.pipe(S.greaterThanOrEqualToBigint(5n));
S.bigint.pipe(S.lessThanBigint(5n));
S.bigint.pipe(S.lessThanOrEqualToBigint(5n));
S.bigint.pipe(S.betweenBigint(-2n, 2n)); // -2n <= x <= 2n

S.bigint.pipe(S.positiveBigint()); // > 0n
S.bigint.pipe(S.nonNegativeBigint()); // >= 0n
S.bigint.pipe(S.negativeBigint()); // < 0n
S.bigint.pipe(S.nonPositiveBigint()); // <= 0n

BigDecimal filters

import * as S from "@effect/schema/Schema";
import * as BigDecimal from "effect/BigDecimal";

S.BigDecimal.pipe(S.greaterThanBigDecimal(BigDecimal.fromNumber(5)));
S.BigDecimal.pipe(S.greaterThanOrEqualToBigDecimal(BigDecimal.fromNumber(5)));
S.BigDecimal.pipe(S.lessThanBigDecimal(BigDecimal.fromNumber(5)));
S.BigDecimal.pipe(S.lessThanOrEqualToBigDecimal(BigDecimal.fromNumber(5)));
S.BigDecimal.pipe(
  S.betweenBigDecimal(BigDecimal.fromNumber(-2), BigDecimal.fromNumber(2))
);

S.BigDecimal.pipe(S.positiveBigDecimal());
S.BigDecimal.pipe(S.nonNegativeBigDecimal());
S.BigDecimal.pipe(S.negativeBigDecimal());
S.BigDecimal.pipe(S.nonPositiveBigDecimal());

Duration filters

import * as S from "@effect/schema/Schema";
import * as Duration from "effect/BigDecimal";

S.Duration.pipe(S.greaterThanDuration("5 seconds"));
S.Duration.pipe(S.greaterThanOrEqualToDuration("5 seconds"));
S.Duration.pipe(S.lessThanDuration("5 seconds"));
S.Duration.pipe(S.lessThanOrEqualToDuration("5 seconds"));
S.Duration.pipe(S.betweenDuration("5 seconds", "10 seconds"));

Array filters

import * as S from "@effect/schema/Schema";

S.array(S.number).pipe(S.maxItems(2)); // max array length
S.array(S.number).pipe(S.minItems(2)); // min array length
S.array(S.number).pipe(S.itemsCount(2)); // exact array length

Branded types

TypeScript's type system is structural, which means that any two types that are structurally equivalent are considered the same. This can cause issues when types that are semantically different are treated as if they were the same.

type UserId = string
type Username = string

const getUser = (id: UserId) => { ... }

const myUsername: Username = "gcanti"

getUser(myUsername) // works fine

In the above example, UserId and Username are both aliases for the same type, string. This means that the getUser function can mistakenly accept a Username as a valid UserId, causing bugs and errors.

To avoid these kinds of issues, the @effect ecosystem provides a way to create custom types with a unique identifier attached to them. These are known as "branded types".

import type * as B from "effect/Brand"

type UserId = string & B.Brand<"UserId">
type Username = string

const getUser = (id: UserId) => { ... }

const myUsername: Username = "gcanti"

getUser(myUsername) // error

By defining UserId as a branded type, the getUser function can accept only values of type UserId, and not plain strings or other types that are compatible with strings. This helps to prevent bugs caused by accidentally passing the wrong type of value to the function.

There are two ways to define a schema for a branded type, depending on whether you:

want to define the schema from scratch
have already defined a branded type via effect/Brand and want to reuse it to define a schema

Defining a schema from scratch

To define a schema for a branded type from scratch, you can use the brand combinator exported by the @effect/schema/Schema module. Here's an example:

import { pipe } from "effect/Function";
import * as S from "@effect/schema/Schema";

const UserId = S.string.pipe(S.brand("UserId"));
type UserId = S.Schema.To<typeof UserId>; // string & Brand<"UserId">

Note that you can use unique symbols as brands to ensure uniqueness across modules / packages:

import { pipe } from "effect/Function";
import * as S from "@effect/schema/Schema";

const UserIdBrand = Symbol.for("UserId");
const UserId = S.string.pipe(S.brand(UserIdBrand));
type UserId = S.Schema.To<typeof UserId>; // string & Brand<typeof UserIdBrand>

Reusing an existing branded type

If you have already defined a branded type using the effect/Brand module, you can reuse it to define a schema using the fromBrand combinator exported by the @effect/schema/Schema module. Here's an example:

import * as B from "effect/Brand";

// the existing branded type
type UserId = string & B.Brand<"UserId">;
const UserId = B.nominal<UserId>();

import { pipe } from "effect/Function";
import * as S from "@effect/schema/Schema";

// Define a schema for the branded type
const UserIdSchema = S.string.pipe(S.fromBrand(UserId));

Native enums

enum Fruits {
  Apple,
  Banana,
}

// $ExpectType Schema<Fruits>
S.enums(Fruits);

Nullables

// $ExpectType Schema<string | null>
S.nullable(S.string);

Unions

@effect/schema/Schema includes a built-in union combinator for composing "OR" types.

// $ExpectType Schema<string | number>
S.union(S.string, S.number);

Union of literals

While the following is perfectly acceptable:

// $ExpectType Schema<"a" | "b" | "c">
const schema = S.union(S.literal("a"), S.literal("b"), S.literal("c"));

It is possible to use literal and pass multiple literals, which is less cumbersome:

// $ExpectType Schema<"a" | "b" | "c">
const schema = S.literal("a", "b", "c");

Under the hood, they are the same, as literal(...literals) will be converted into a union.

Discriminated unions

TypeScript reference: https://www.typescriptlang.org/docs/handbook/2/narrowing.html#discriminated-unions

Discriminated unions in TypeScript are a way of modeling complex data structures that may take on different forms based on a specific set of conditions or properties. They allow you to define a type that represents multiple related shapes, where each shape is uniquely identified by a shared discriminant property.

In a discriminated union, each variant of the union has a common property, called the discriminant. The discriminant is a literal type, which means it can only have a finite set of possible values. Based on the value of the discriminant property, TypeScript can infer which variant of the union is currently in use.

Here is an example of a discriminated union in TypeScript:

type Circle = {
  readonly kind: "circle";
  readonly radius: number;
};

type Square = {
  readonly kind: "square";
  readonly sideLength: number;
};

type Shape = Circle | Square;

This code defines a discriminated union using the @effect/schema library:

import * as S from "@effect/schema/Schema";

const Circle = S.struct({
  kind: S.literal("circle"),
  radius: S.number,
});

const Square = S.struct({
  kind: S.literal("square"),
  sideLength: S.number,
});

const Shape = S.union(Circle, Square);

The literal combinator is used to define the discriminant property with a specific string literal value.

Two structs are defined for Circle and Square, each with their own properties. These structs represent the variants of the union.

Finally, the union combinator is used to create a schema for the discriminated union Shape, which is a union of Circle and Square.

How to transform a simple union into a discriminated union

If you're working on a TypeScript project and you've defined a simple union to represent a particular input, you may find yourself in a situation where you're not entirely happy with how it's set up. For example, let's say you've defined a Shape union as a combination of Circle and Square without any special property:

import * as S from "@effect/schema/Schema";

const Circle = S.struct({
  radius: S.number,
});

const Square = S.struct({
  sideLength: S.number,
});

const Shape = S.union(Circle, Square);

To make your code more manageable, you may want to transform the simple union into a discriminated union. This way, TypeScript will be able to automatically determine which member of the union you're working with based on the value of a specific property.

To achieve this, you can add a special property to each member of the union, which will allow TypeScript to know which type it's dealing with at runtime. Here's how you can transform the Shape schema into another schema that represents a discriminated union:

import * as S from "@effect/schema/Schema";

const Circle = S.struct({
  radius: S.number,
});

const Square = S.struct({
  sideLength: S.number,
});

const DiscriminatedShape = S.union(
  Circle.pipe(
    S.transform(
      Circle.pipe(S.extend(S.struct({ kind: S.literal("circle") }))), // Add a "kind" property with the literal value "circle" to Circle
      (circle) => ({ ...circle, kind: "circle" as const }), // Add the discriminant property to Circle
      ({ kind: _kind, ...rest }) => rest // Remove the discriminant property
    )
  ),
  Square.pipe(
    S.transform(
      Square.pipe(S.extend(S.struct({ kind: S.literal("square") }))), // Add a "kind" property with the literal value "square" to Square
      (square) => ({ ...square, kind: "square" as const }), // Add the discriminant property to Square
      ({ kind: _kind, ...rest }) => rest // Remove the discriminant property
    )
  )
);

expect(S.parseSync(DiscriminatedShape)({ radius: 10 })).toEqual({
  kind: "circle",
  radius: 10,
});

expect(S.parseSync(DiscriminatedShape)({ sideLength: 10 })).toEqual({
  kind: "square",
  sideLength: 10,
});

In this example, we use the extend function to add a "kind" property with a literal value to each member of the union. Then we use transform to add the discriminant property and remove it afterwards. Finally, we use union to combine the transformed schemas into a discriminated union.

However, when we use the schema to encode a value, we want the output to match the original input shape. Therefore, we must remove the discriminant property we added earlier from the encoded value to match the original shape of the input.

The previous solution works perfectly and shows how we can add and remove properties to our schema at will, making it easier to consume the result within our domain model. However, it requires a lot of boilerplate. Fortunately, there is an API called attachPropertySignature designed specifically for this use case, which allows us to achieve the same result with much less effort:

const Circle = S.struct({ radius: S.number });
const Square = S.struct({ sideLength: S.number });
const DiscriminatedShape = S.union(
  Circle.pipe(S.attachPropertySignature("kind", "circle")),
  Square.pipe(S.attachPropertySignature("kind", "square"))
);

// parsing
expect(S.parseSync(DiscriminatedShape)({ radius: 10 })).toEqual({
  kind: "circle",
  radius: 10,
});

// encoding
expect(
  S.encodeSync(DiscriminatedShape)({
    kind: "circle",
    radius: 10,
  })
).toEqual({ radius: 10 });

Tuples

// $ExpectType Schema<readonly [string, number]>
S.tuple(S.string, S.number);

Append a required element

// $ExpectType Schema<readonly [string, number, boolean]>
S.tuple(S.string, S.number).pipe(S.element(S.boolean));

Append an optional element

// $ExpectType Schema<readonly [string, number, boolean?]>
S.tuple(S.string, S.number).pipe(S.optionalElement(S.boolean));

Append a rest element

// $ExpectType Schema<readonly [string, number, ...boolean[]]>
S.tuple(S.string, S.number).pipe(S.rest(S.boolean));

Arrays

// $ExpectType Schema<readonly number[]>
S.array(S.number);

Mutable Arrays

By default, when you use S.array, it generates a type marked as readonly. The mutable combinator is a useful function for creating a new schema with a mutable type in a shallow manner:

// $ExpectType Schema<number[]>
S.mutable(S.array(S.number));

Non empty arrays

// $ExpectType Schema<readonly [number, ...number[]]>
S.nonEmptyArray(S.number);

Structs

// $ExpectType Schema<{ readonly a: string; readonly b: number; }>
S.struct({ a: S.string, b: S.number });

Mutable Properties

By default, when you use S.struct, it generates a type with properties that are marked as readonly. The mutable combinator is a useful function for creating a new schema with properties made mutable in a shallow manner:

// $ExpectType Schema<{ a: string; b: number; }>
S.mutable(S.struct({ a: S.string, b: S.number }));

Optional fields

// $ExpectType Schema<{ readonly a: string; readonly b: number; readonly c?: boolean; }>
S.struct({ a: S.string, b: S.number, c: S.optional(S.boolean) });

Note. The optional constructor only exists to be used in combination with the struct API to signal an optional field and does not have a broader meaning. This means that it is only allowed to use it as an outer wrapper of a Schema and it cannot be followed by other combinators, for example this type of operation is prohibited:

S.struct({
  // the use of S.optional should be the last step in the pipeline and not preceeded by other combinators like S.nullable
  c: S.boolean.pipe(S.optional, S.nullable), // type checker error
});

and it must be rewritten like this:

S.struct({
  c: S.boolean.pipe(S.nullable, S.optional), // ok
});

Default values

Optional fields can be configured to accept a default value, making the field optional in input and required in output:

// $ExpectType Schema<{ readonly a?: number; }, { readonly a: number; }>
const schema = S.struct({ a: S.optional(S.number).withDefault(() => 0) });

const parse = S.parseSync(schema);

parse({}); // { a: 0 }
parse({ a: 1 }); // { a: 1 }

const encode = S.encodeSync(schema);

encode({ a: 0 }); // { a: 0 }
encode({ a: 1 }); // { a: 1 }

Optional fields as `Option`s

Optional fields can be configured to transform a value of type A into Option<A>, making the field optional in input and required in output:

import * as O from "effect/Option"

// $ExpectType Schema<{ readonly a?: number; }, { readonly a: Option<number>; }>
const schema = S.struct({ a: S.optional(S.number).toOption() });

const parse = S.parseSync(schema)

parse({}) // { a: none() }
parse({ a: 1 }) // { a: some(1) }

const encode = S.encodeSync(schema)

encode({ a: O.none() }) // {}
encode({ a: O.some(1) }) // { a: 1 }

Renaming Properties

To rename one or more properties, you can utilize the rename API:

import * as Schema from "@effect/schema/Schema";

// Original Schema
const originalSchema = Schema.struct({ a: Schema.string, b: Schema.number });

// Renaming the "a" property to "c"
const renamedSchema = Schema.rename(originalSchema, { a: "c" });

Schema.parseSync(renamedSchema)({ a: "a", b: 1 }); // Output: { c: "a", b: 1 }

In the example above, we have an original schema with properties "a" and "b." Using the rename API, we create a new schema where we rename the "a" property to "c." The resulting schema, when used with Schema.parseSync, transforms the input object by renaming the specified property.

Classes

When working with schemas, you have a choice beyond the S.struct constructor. You can leverage the power of classes through the Class utility, which comes with its own set of advantages tailored to common use cases.

The Benefits of Using Classes

Classes offer several features that simplify the schema creation process:

All-in-One Definition: With classes, you can define both a schema and an opaque type simultaneously.
Shared Functionality: You can incorporate shared functionality using class methods or getters.
Value Equality and Hashing: Utilize the built-in capability for checking value equality and applying hashing (thanks to Class implementing Data.Case).

Let's dive into an illustrative example to better understand how classes work:

import * as S from "@effect/schema/Schema";

// Define your schema by providing the type to `Class` and the desired fields
class Person extends S.Class<Person>()({
  id: S.number,
  name: S.string.pipe(S.nonEmpty()),
}) {}

Validation and Instantiation

The class constructor serves as a validation and instantiation tool. It ensures that the provided properties meet the schema requirements:

const tim = new Person({ id: 1, name: "Tim" });

Keep in mind that it throws an error for invalid properties:

new Person({ id: 1, name: "" });
/* throws
error(s) found
└─ ["name"]
   └─ Expected a non empty string, actual ""
*/

Custom Getters and Methods

For more flexibility, you can also introduce custom getters and methods:

import * as S from "@effect/schema/Schema";

class Person extends S.Class<Person>()({
  id: S.number,
  name: S.string.pipe(S.nonEmpty()),
}) {
  get upperName() {
    return this.name.toUpperCase();
  }
}

const john = new Person({ id: 1, name: "John" });

console.log(john.upperName); // "JOHN"

The class constructor itself is a Schema, and can be assigned/provided anywhere a Schema is expected. There is also a .struct property, which can be used when the class prototype is not required.

// $ExpectType Schema<{ readonly id: number; name: string; }, Person>
S.suspend(() => Person);

// $ExpectType Schema<{ readonly id: number; name: string; }, { readonly id: number; name: string; }>
Person.struct;

Tagged Class variants

You can also create classes that extend TaggedClass & TaggedError from the effect/Data module:

import * as S from "@effect/schema/Schema";

class TaggedPerson extends S.TaggedClass<TaggedPerson>()("TaggedPerson", {
  name: S.string,
}) {}

class HttpError extends S.TaggedError<HttpError>()("HttpError", {
  status: S.number,
}) {}

const joe = new TaggedPerson({ name: "Joe" });
console.log(joe._tag); // "TaggedPerson"

const error = new HttpError({ status: 404 });
console.log(error._tag); // "HttpError"
console.log(error.stack); // access the stack trace

Extending existing Classes

In situations where you need to augment your existing class with more fields, the built-in extend utility comes in handy:

import * as S from "@effect/schema/Schema";

class Person extends S.Class<Person>()({
  id: S.number,
  name: S.string.pipe(S.nonEmpty()),
}) {
  get upperName() {
    return this.name.toUpperCase();
  }
}

class PersonWithAge extends Person.extend<PersonWithAge>()({
  age: S.number,
}) {
  get isAdult() {
    return this.age >= 18;
  }
}

Transforms

You have the option to enhance a class with (effectful) transforms. This becomes valuable when you want to enrich or validate an entity sourced from a data store.

import * as ParseResult from "@effect/schema/ParseResult";
import * as S from "@effect/schema/Schema";
import * as Effect from "effect/Effect";
import * as Option from "effect/Option";

export class Person extends S.Class<Person>()({
  id: S.number,
  name: S.string,
}) {}

console.log(S.parseSync(Person)({ id: 1, name: "name" }));
/*
Output:
Person { id: 1, name: 'name' }
*/

function getAge(id: number): Effect.Effect<never, Error, number> {
  return Effect.succeed(id + 2);
}

export class PersonWithTransform extends Person.transform<PersonWithTransform>()(
  {
    age: S.optional(S.number).toOption(),
  },
  (input) =>
    Effect.mapBoth(getAge(input.id), {
      onFailure: (e) =>
        ParseResult.parseError([
          ParseResult.type(S.string.ast, input.id, e.message),
        ]),
      // must return { age: Option<number> }
      onSuccess: (age) => ({ ...input, age: Option.some(age) }),
    }),
  ParseResult.succeed
) {}

S.parsePromise(PersonWithTransform)({ id: 1, name: "name" }).then(console.log);
/*
Output:
PersonWithTransform {
  id: 1,
  name: 'name',
  age: { _id: 'Option', _tag: 'Some', value: 3 }
}
*/

export class PersonWithTransformFrom extends Person.transformFrom<PersonWithTransformFrom>()(
  {
    age: S.optional(S.number).toOption(),
  },
  (input) =>
    Effect.mapBoth(getAge(input.id), {
      onFailure: (e) =>
        ParseResult.parseError([
          ParseResult.type(S.string.ast, input, e.message),
        ]),
      // must return { age?: number }
      onSuccess: (age) => (age > 18 ? { ...input, age } : { ...input }),
    }),
  ParseResult.succeed
) {}

S.parsePromise(PersonWithTransformFrom)({ id: 1, name: "name" }).then(
  console.log
);
/*
Output:
PersonWithTransformFrom {
  id: 1,
  name: 'name',
  age: { _id: 'Option', _tag: 'None' }
}
*/

The decision of which API to use, either transform or transformFrom, depends on when you wish to execute the transformation:

Using transform:
- The transformation occurs at the end of the process.
- It expects you to provide a value of type { age: Option<number> }.
- After processing the initial input, the new transformation comes into play, and you need to ensure the final output adheres to the specified structure.
Using transformFrom:
- The new transformation starts as soon as the initial input is handled.
- You should provide a value { age?: number }.
- Based on this fresh input, the subsequent transformation { age: S.optional(S.number).toOption() } is executed.
- This approach allows for immediate handling of the input, potentially influencing the subsequent transformations.

Pick

The pick operation is used to select specific properties from a schema.

import * as S from "@effect/schema/Schema";

// $ExpectType Schema<{ readonly a: string; }>
S.struct({ a: S.string, b: S.number, c: S.boolean }).pipe(S.pick("a"));

// $ExpectType Schema<{ readonly a: string; readonly c: boolean; }>
S.struct({ a: S.string, b: S.number, c: S.boolean }).pipe(S.pick("a", "c"));

Omit

The omit operation is employed to exclude certain properties from a schema.

import * as S from "@effect/schema/Schema";

// $ExpectType Schema<{ readonly b: number; readonly c: boolean; }>
S.struct({ a: S.string, b: S.number, c: S.boolean }).pipe(S.omit("a"));

// $ExpectType Schema<{ readonly b: number; }>
S.struct({ a: S.string, b: S.number, c: S.boolean }).pipe(S.omit("a", "c"));

Partial

The partial operation makes all properties within a schema optional.

// $ExpectType Schema<{ readonly a?: string; readonly b?: number; }>
S.partial(S.struct({ a: S.string, b: S.number }));

Required

The required operation ensures that all properties in a schema are mandatory.

// $ExpectType Schema<{ readonly a: string; readonly b: number; }>
S.required(S.struct({ a: S.optional(S.string), b: S.optional(S.number) }));

Records

String keys

// $ExpectType Schema<{ readonly [x: string]: string; }>
S.record(S.string, S.string);

// $ExpectType Schema<{ readonly a: string; readonly b: string; }>
S.record(S.union(S.literal("a"), S.literal("b")), S.string);

// $ExpectType Schema<{ readonly [x: string]: string; }>
S.record(S.string.pipe(S.minLength(2)), S.string);

Symbol keys

// $ExpectType Schema<{ readonly [x: symbol]: string; }>
S.record(S.symbolFromSelf, S.string);

Template literal keys

// $ExpectType Schema<{ readonly [x: `a${string}`]: string; }>
S.record(S.templateLiteral(S.literal("a"), S.string), S.string);

Mutable Records

By default, when you use S.record, it generates a type marked as readonly. The mutable combinator is a useful function for creating a new schema with a mutable type in a shallow manner:

// $ExpectType Schema<{ [x: string]: string; }>
S.mutable(S.record(S.string, S.string););

Extend

The extend combinator allows you to add additional fields or index signatures to an existing Schema.

// $ExpectType Schema<{ readonly [x: string]: string; readonly a: string; readonly b: string; readonly c: string; }>
S.struct({ a: S.string, b: S.string }).pipe(
  S.extend(S.struct({ c: S.string })), // <= you can add more fields
  S.extend(S.record(S.string, S.string)) // <= you can add index signatures
);

Compose

Combining and reusing schemas is a common requirement, the compose combinator allows you to do just that. It enables you to combine two schemas, Schema<A, B> and Schema<B, C>, into a single schema Schema<A, C>:

// $ExpectType Schema<string, readonly string[]>
const schema1 = S.split(S.string, ",");

// $ExpectType Schema<readonly string[], readonly number[]>
const schema2 = S.array(S.NumberFromString);

// $ExpectType Schema<string, readonly number[]>
const composedSchema = S.compose(schema1, schema2);

In this example, we have two schemas, schema1 and schema2. The first schema, schema1, takes a string and splits it into an array using a comma as the delimiter. The second schema, schema2, transforms an array of strings into an array of numbers.

Now, by using the compose combinator, we can create a new schema, composedSchema, that combines the functionality of both schema1 and schema2. This allows us to parse a string and directly obtain an array of numbers as a result.

InstanceOf

In the following section, we demonstrate how to use the instanceOf combinator to create a Schema for a class instance.

class Test {
  constructor(readonly name: string) {}
}

// $ExpectType Schema<Test>
S.instanceOf(Test);

Recursive types

The suspend combinator is useful when you need to define a Schema that depends on itself, like in the case of recursive data structures. In this example, the Category schema depends on itself because it has a field subcategories that is an array of Category objects.

import * as S from "@effect/schema/Schema";

interface Category {
  readonly name: string;
  readonly subcategories: ReadonlyArray<Category>;
}

const Category: S.Schema<Category> = S.struct({
  name: S.string,
  subcategories: S.array(S.suspend(() => Category)),
});

Here's an example of two mutually recursive schemas, Expression and Operation, that represent a simple arithmetic expression tree.

import * as S from "@effect/schema/Schema";

interface Expression {
  readonly type: "expression";
  readonly value: number | Operation;
}

interface Operation {
  readonly type: "operation";
  readonly operator: "+" | "-";
  readonly left: Expression;
  readonly right: Expression;
}

const Expression: S.Schema<Expression> = S.struct({
  type: S.literal("expression"),
  value: S.union(
    S.number,
    S.suspend(() => Operation)
  ),
});

const Operation: S.Schema<Operation> = S.struct({
  type: S.literal("operation"),
  operator: S.union(S.literal("+"), S.literal("-")),
  left: Expression,
  right: Expression,
});

Transformations

In some cases, we may need to transform the output of a schema to a different type. For instance, we may want to parse a string into a number, or we may want to transform a date string into a Date object.

To perform these kinds of transformations, the @effect/schema library provides the transform combinator.

transform

declare const transform: <A, B, C, D>(
  from: Schema<A, B>,
  to: Schema<C, D>,
  decode: (b: B) => C,
  encode: (c: C) => B
) => Schema<A, D>;

flowchart TD
  schema1["from: Schema&lt;A, B&gt;"]
  schema2["to: Schema&lt;C, D&gt;"]
  schema1--decode: B -> C-->schema2
  schema2--encode: C -> B-->schema1

The transform combinator takes a source schema, a target schema, a transformation function from the source type to the target type, and a reverse transformation function from the target type back to the source type. It returns a new schema that applies the transformation function to the output of the original schema before returning it. If the original schema fails to parse a value, the transformed schema will also fail.

import * as S from "@effect/schema/Schema";

// use the transform combinator to convert the string schema into the tuple schema
export const transformedSchema: S.Schema<string, readonly [string]> =
  S.transform(
    S.string,
    S.tuple(S.string),
    // define a function that converts a string into a tuple with one element of type string
    (s) => [s] as const,
    // define a function that converts a tuple with one element of type string into a string
    ([s]) => s
  );

In the example above, we defined a schema for the string type and a schema for the tuple type [string]. We also defined the functions decode and encode that convert a string into a tuple and a tuple into a string, respectively. Then, we used the transform combinator to convert the string schema into a schema for the tuple type [string]. The resulting schema can be used to parse values of type string into values of type [string].

Non-strict option

If you need to be less restrictive in your decode and encode functions, you can make use of the { strict: false } option:

declare const transform: <A, B, C, D>(
  from: Schema<A, B>,
  to: Schema<C, D>,
  decode: (b: B) => unknown, // Less strict constraint
  encode: (c: C) => unknown, // Less strict constraint
  options: { strict: false }
) => Schema<A, D>;

This is useful when you want to relax the type constraints imposed by the decode and encode functions, making them more permissive.

transformOrFail

The transformOrFail combinator works in a similar way, but allows the transformation function to return a ParseResult object, which can either be a success or a failure.

import * as ParseResult from "@effect/schema/ParseResult";
import * as S from "@effect/schema/Schema";

export const transformedSchema: S.Schema<string, boolean> = S.transformOrFail(
  S.string,
  S.boolean,
  // define a function that converts a string into a boolean
  (s) =>
    s === "true"
      ? ParseResult.succeed(true)
      : s === "false"
        ? ParseResult.succeed(false)
        : ParseResult.fail(ParseResult.type(S.literal("true", "false").ast, s)),
  // define a function that converts a boolean into a string
  (b) => ParseResult.succeed(String(b))
);

The transformation may also be async:

import * as ParseResult from "@effect/schema/ParseResult";
import * as S from "@effect/schema/Schema";
import * as TreeFormatter from "@effect/schema/TreeFormatter";
import * as Effect from "effect/Effect";

const api = (url: string) =>
  Effect.tryPromise({
    try: () =>
      fetch(url).then((res) => {
        if (res.ok) {
          return res.json() as Promise<unknown>;
        }
        throw new Error(String(res.status));
      }),
    catch: (e) => new Error(String(e)),
  });

const PeopleId = S.string.pipe(S.brand("PeopleId"));

const PeopleIdFromString = S.transformOrFail(
  S.string,
  PeopleId,
  (s, _, ast) =>
    Effect.mapBoth(api(`https://swapi.dev/api/people/${s}`), {
      onFailure: (e) =>
        ParseResult.parseError([ParseResult.type(ast, s, e.message)]),
      onSuccess: () => s,
    }),
  ParseResult.succeed
);

const parse = (id: string) =>
  Effect.mapError(S.parse(PeopleIdFromString)(id), (e) =>
    TreeFormatter.formatErrors(e.errors)
  );

Effect.runPromiseExit(parse("1")).then(console.log);
/*
Output:
{ _id: 'Exit', _tag: 'Success', value: '1' }
*/

Effect.runPromiseExit(parse("fail")).then(console.log);
/*
Output:
{
  _id: 'Exit',
  _tag: 'Failure',
  cause: {
    _id: 'Cause',
    _tag: 'Fail',
    failure: 'error(s) found\n└─ Error: 404'
  }
}
*/

String transformations

split

The split combinator allows splitting a string into an array of strings.

import * as S from "@effect/schema/Schema";

// $ExpectType Schema<string, string[]>
const schema = S.string.pipe(S.split(","));
const parse = S.parseSync(schema);

parse(""); // [""]
parse(","); // ["", ""]
parse("a,"); // ["a", ""]
parse("a,b"); // ["a", "b"]

Trim

The Trim schema allows removing whitespaces from the beginning and end of a string.

import * as S from "@effect/schema/Schema";

// $ExpectType Schema<string, string>
const schema = S.Trim;
const parse = S.parseSync(schema);

parse("a"); // "a"
parse(" a"); // "a"
parse("a "); // "a"
parse(" a "); // "a"

Note. If you were looking for a combinator to check if a string is trimmed, check out the trimmed combinator.

Lowercase

The Lowercase schema converts a string to lowercase.

import * as S from "@effect/schema/Schema";

// const schema: S.Schema<string, string>
const schema = S.Lowercase;
const parse = S.parseSync(schema);

parse("A"); // "a"
parse(" AB"); // " ab"
parse("Ab "); // "ab "
parse(" ABc "); // " abc "

Note. If you were looking for a combinator to check if a string is lowercased, check out the lowercased combinator.

ParseJson

The ParseJson schema offers a method to convert JSON strings into the unknown type using the underlying functionality of JSON.parse. It also employs JSON.stringify for encoding.

import * as S from "@effect/schema/Schema";

// $ExpectType Schema<string, unknown>
const schema = S.ParseJson;
const parse = S.parseSync(schema);

parse("{}"); // {}
parse(`{"a":"b"}`); // { "a": "b" }
parse(""); // throws Unexpected end of JSON input

You can also compose the ParseJson schema with other schemas to refine the parsing result:

import * as S from "@effect/schema/Schema";

// $ExpectType Schema<string, { readonly a: number; }>
const schema = S.ParseJson.pipe(S.compose(S.struct({ a: S.number })));

In this example, we've composed the ParseJson schema with a struct schema to ensure that the result will have a specific shape, including an object with a numeric property "a".

Alternatively, you can achieve the same result by using the equivalent built-in combinator fromJson:

import * as S from "@effect/schema/Schema";

// $ExpectType Schema<string, { readonly a: number; }>
const schema = S.fromJson(S.struct({ a: S.number }));

Number transformations

NumberFromString

Transforms a string into a number by parsing the string using parseFloat.

The following special string values are supported: "NaN", "Infinity", "-Infinity".

import * as S from "@effect/schema/Schema";

// $ExpectType Schema<string, number>
const schema = S.NumberFromString;
const parse = S.parseSync(schema);

// success cases
parse("1"); // 1
parse("-1"); // -1
parse("1.5"); // 1.5
parse("NaN"); // NaN
parse("Infinity"); // Infinity
parse("-Infinity"); // -Infinity

// failure cases
parse("a"); // throws

clamp

Clamps a number between a minimum and a maximum value.

import * as S from "@effect/schema/Schema";

// $ExpectType Schema<number, number>
const schema = S.number.pipe(S.clamp(-1, 1)); // clamps the input to -1 <= x <= 1

const parse = S.parseSync(schema);
parse(-3); // -1
parse(0); // 0
parse(3); // 1

BigDecimal transformations

BigDecimal

Transforms a string into a BigDecimal.

import * as S from "@effect/schema/Schema";

// $ExpectType Schema<string, BigDecimal>
const schema = S.BigDecimal;
const parse = S.parseSync(schema);

parse(".124"); // BigDecimal(.124)

BigDecimalFromNumber

Transforms a number into a BigDecimal.

[!WARNING] Warning: When encoding, this Schema will produce incorrect results if the BigDecimal exceeds the 64-bit range of a number.

import * as S from "@effect/schema/Schema";

// $ExpectType Schema<number, BigDecimal>
const schema = S.BigDecimalFromNumber;
const parse = S.parseSync(schema);

parse(0.111); // BigDecimal(.111)

clampBigDecimal

Clamps a BigDecimal between a minimum and a maximum value.

import * as S from "@effect/schema/Schema";
import * as BigDecimal from "effect/BigDecimal";

// $ExpectType Schema<BigDecimal.BigDecimal, BigDecimal.BigDecimal>
const schema = S.BigDecimal.pipe(
  S.clampBigDecimal(BigDecimal.fromNumber(-1), BigDecimal.fromNumber(1))
);

const parse = S.parseSync(schema);
parse("-2"); // BigDecimal(-1)
parse("0"); // BigDecimal(0)
parse("3"); // BigDecimal(1)

negateBigDecimal

Negates a BigDecimal.

import * as S from "@effect/schema/Schema";
import * as BigDecimal from "effect/BigDecimal";

// $ExpectType Schema<BigDecimal.BigDecimal, BigDecimal.BigDecimal>
const schema = S.BigDecimal.pipe(S.negateBigDecimal);

const parse = S.parseSync(schema);
parse("-2"); // BigDecimal(2)
parse("0"); // BigDecimal(0)
parse("3"); // BigDecimal(-3)

Duration transformations

Duration

Converts an hrtime(i.e. [seconds: number, nanos: number]) into a Duration.

import * as S from "@effect/schema/Schema";
import * as Duration from "effect/Duration";

// $ExpectType Schema<number, Duration>
const schema = S.Duration;

const parse = S.parseSync(schema);
parse([0, 0]); // 0 seconds
parse([5000, 0]); // 5 seconds

DurationFromNumber

Converts a number into a Duration where the number represents the number of milliseconds.

import * as S from "@effect/schema/Schema";
import * as Duration from "effect/Duration";

// $ExpectType Schema<number, Duration>
const schema = S.DurationFromNumber;

const parse = S.parseSync(schema);
parse(0); // 0 seconds
parse(5000); // 5 seconds

DurationFromBigint

Converts a bigint into a Duration where the number represents the number of nanoseconds.

import * as S from "@effect/schema/Schema";
import * as Duration from "effect/Duration";

// $ExpectType Schema<bigint, Duration>
const schema = S.DurationFromBigint;

const parse = S.parseSync(schema);
parse(0n); // 0 seconds
parse(5000000000n); // 5 seconds

clampDuration

Clamps a Duration between a minimum and a maximum value.

import * as S from "@effect/schema/Schema";
import * as Duration from "effect/Duration";

// $ExpectType Schema<Duration.Duration, Duration.Duration>
const schema = S.DurationFromSelf.pipe(
  S.clampDuration("5 seconds", "10 seconds")
);

const parse = S.parseSync(schema);
parse(Duration.decode("2 seconds")); // 5 seconds
parse(Duration.decode("6 seconds")); // 6 seconds
parse(Duration.decode("11 seconds")); // 10 seconds

Secret transformations

Secret

Converts a string into a Secret.

import * as S from "@effect/schema/Schema";
import * as Secret from "effect/Secret";

// $ExpectType Schema<string, Secret>
const schema = S.Secret;

const parse = S.parseSync(schema);
parse("keep it secret, keep it safe"); // Secret

Symbol transformations

symbol

Transforms a string into a symbol by parsing the string using Symbol.for.

import * as S from "@effect/schema/Schema";

// $ExpectType Schema<string, symbol>
const schema = S.symbol;
const parse = S.parseSync(schema);

parse("a"); // Symbol.for("a")

Bigint transformations

bigint

Transforms a string into a bigint by parsing the string using BigInt.

import * as S from "@effect/schema/Schema";

// $ExpectType Schema<string, bigint>
const schema = S.bigint;
const parse = S.parseSync(schema);

// success cases
parse("1"); // 1n
parse("-1"); // -1n

// failure cases
parse("a"); // throws
parse("1.5"); // throws
parse("NaN"); // throws
parse("Infinity"); // throws
parse("-Infinity"); // throws

BigintFromNumber

Transforms a number into a bigint by parsing the number using BigInt.

import * as S from "@effect/schema/Schema";

// const schema: S.Schema<number, bigint>
const schema = S.BigintFromNumber;
const parse = S.parseSync(schema);
const encode = S.encodeSync(schema);

// success cases
parse(1); // 1n
parse(-1); // -1n
encode(1n); // 1
encode(-1n); // -1

// failure cases
parse(1.5); // throws
parse(NaN); // throws
parse(Infinity); // throws
parse(-Infinity); // throws
encode(BigInt(Number.MAX_SAFE_INTEGER) + 1n); // throws
encode(BigInt(Number.MIN_SAFE_INTEGER) - 1n); // throws

clamp

Clamps a bigint between a minimum and a maximum value.

import * as S from "@effect/schema/Schema";

// $ExpectType Schema<bigint, bigint>
const schema = S.bigint.pipe(S.clampBigint(-1n, 1n)); // clamps the input to -1n <= x <= 1n

const parse = S.parseSync(schema);
parse(-3n); // -1n
parse(0n); // 0n
parse(3n); // 1n

Boolean transformations

not

Negates a boolean value.

import * as S from "@effect/schema/Schema";

// $ExpectType Schema<boolean, boolean>
const schema = S.boolean.pipe(S.not);

const parse = S.parseSync(schema);
parse(true); // false
parse(false); // true

Date transformations

Date

Transforms a string into a valid Date, ensuring that invalid dates, such as new Date("Invalid Date"), are rejected.

import * as S from "@effect/schema/Schema";

// $ExpectType Schema<string, Date>
const schema = S.Date;
const parse = S.parseSync(schema);

parse("1970-01-01T00:00:00.000Z"); // new Date(0)

parse("a"); // throws

const validate = S.validateSync(schema);

validate(new Date(0)); // new Date(0)
validate(new Date("Invalid Date")); // throws

Interop with `effect/Data`

The effect/Data module in the Effect ecosystem serves as a utility module that simplifies the process of comparing values for equality without the need for explicit implementations of the Equal and Hash interfaces. It provides convenient APIs that automatically generate default implementations for equality checks, making it easier for developers to perform equality comparisons in their applications.

import * as Data from "effect/Data";
import * as Equal from "effect/Equal";

const person1 = Data.struct({ name: "Alice", age: 30 });
const person2 = Data.struct({ name: "Alice", age: 30 });

console.log(Equal.equals(person1, person2)); // true

You can use the Schema.data(schema) combinator to build a schema from an existing schema that can decode a value A to a value Data<A>:

/*
S.Schema<{
    readonly name: string;
    readonly age: number;
}, Data.Data<{
    readonly name: string;
    readonly age: number;
}>>
*/
const schema = S.data(
  S.struct({
    name: S.string,
    age: S.number,
  })
);

const decode = S.decode(schema);

const person1 = decode({ name: "Alice", age: 30 });
const person2 = decode({ name: "Alice", age: 30 });

console.log(Equal.equals(person1, person2)); // true

Option

Parsing from nullable fields

The optionFromNullable combinator in @effect/schema/Schema allows you to specify that a field in a schema is of type Option<A> and can be parsed from a required nullable field A | null. This is particularly useful when working with JSON data that may contain null values for optional fields.

When parsing a nullable field, the option combinator follows these conversion rules:

null parses to None
A parses to Some<A>

Here's an example that demonstrates how to use the optionFromNullable combinator:

import * as Schema from "@effect/schema/Schema";
import { Option } from "effect";

/*
const schema: Schema<{
    readonly a: string;
    readonly b: number | null;
}, {
    readonly a: string;
    readonly b: Option<number>;
}>
*/
const schema = Schema.struct({
  a: Schema.string,
  b: Schema.optionFromNullable(Schema.number),
});

// parsing
const parseOrThrow = Schema.parseSync(schema);

console.log(parseOrThrow({ a: "hello", b: null }));
/*
Output:
{
  a: "hello",
  b: {
    _id: "Option",
    _tag: "None"
  }
}
*/

console.log(parseOrThrow({ a: "hello", b: 1 }));
/*
Output:
{
  a: "hello",
  b: {
    _id: "Option",
    _tag: "Some",
    value: 1
  }
}
*/

parseOrThrow({ a: "hello", b: undefined });
/*
throws:
Error: error(s) found
└─ ["b"]
   ├─ union member
   │  └─ Expected null, actual undefined
   └─ union member
      └─ Expected number, actual undefined
*/

parseOrThrow({ a: "hello" });
/*
throws:
Error: error(s) found
└─ ["b"]
   └─ is missing
*/

// encoding
const encodeOrThrow = Schema.encodeSync(schema);

console.log(encodeOrThrow({ a: "hello", b: Option.none() }));
/*
Output:
{
  a: "hello",
  b: null
}
*/

console.log(encodeOrThrow({ a: "hello", b: Option.some(1) }));
/*
Output:
{
  a: "hello",
  b: 1
}
*/

ReadonlySet

In the following section, we demonstrate how to use the readonlySet combinator to parse a ReadonlySet from an array of values.

import * as S from "@effect/schema/Schema";

// $ExpectType Schema<readonly number[], ReadonlySet<number>>
const schema = S.readonlySet(S.number); // define a schema for ReadonlySet with number values
const parse = S.parseSync(schema);

parse([1, 2, 3]); // new Set([1, 2, 3])

ReadonlyMap

In the following section, we demonstrate how to use the readonlyMap combinator to parse a ReadonlyMap from an array of entries.

import * as S from "@effect/schema/Schema";

// $ExpectType Schema<readonly (readonly [number, string])[], ReadonlyMap<number, string>>
const schema = S.readonlyMap(S.number, S.string); // define the schema for ReadonlyMap with number keys and string values
const parse = S.parseSync(schema);

parse([
  [1, "a"],
  [2, "b"],
  [3, "c"],
]); // new Map([[1, "a"], [2, "b"], [3, "c"]])

Technical overview

Understanding Schemas

A schema is a description of a data structure that can be used to generate various artifacts from a single declaration.

From a technical point of view a schema is just a typed wrapper of an AST value:

interface Schema<I, A> {
  readonly ast: AST;
}

The AST type represents a tiny portion of the TypeScript AST, roughly speaking the part describing ADTs (algebraic data types), i.e. products (like structs and tuples) and unions, plus a custom transformation node.

This means that you can define your own schema constructors / combinators as long as you are able to manipulate the AST value accordingly, let's see an example.

Say we want to define a pair schema constructor, which takes a Schema<A> as input and returns a Schema<readonly [A, A]> as output.

First of all we need to define the signature of pair

import * as S from "@effect/schema/Schema";

declare const pair: <A>(schema: S.Schema<A>) => S.Schema<readonly [A, A]>;

Then we can implement the body using the APIs exported by the @effect/schema/AST module:

import * as S from "@effect/schema/Schema";
import * as AST from "@effect/schema/AST";
import * as Option from "effect/Option";

const pair = <A>(schema: S.Schema<A>): S.Schema<readonly [A, A]> => {
  const element = AST.createElement(
    schema.ast, // <= the element type
    false // <= is optional?
  );
  const tuple = AST.createTuple(
    [element, element], // <= elements definitions
    Option.none(), // <= rest element
    true // <= is readonly?
  );
  return S.make(tuple); // <= wrap the AST value in a Schema
};

This example demonstrates the use of the low-level APIs of the AST module, however, the same result can be achieved more easily and conveniently by using the high-level APIs provided by the Schema module.

const pair = <A>(schema: S.Schema<A>): S.Schema<readonly [A, A]> =>
  S.tuple(schema, schema);

Annotations

One of the fundamental requirements in the design of @effect/schema is that it is extensible and customizable. Customizations are achieved through "annotations". Each node contained in the AST of @effect/schema/AST contains an annotations: Record<symbol, unknown> field that can be used to attach additional information to the schema.

Let's see some examples:

import * as S from "@effect/schema/Schema";

const Password =
  // initial schema, a string
  S.string.pipe(
    // add an error message for non-string values (annotation)
    S.message(() => "not a string"),
    // add a constraint to the schema, only non-empty strings are valid
    // and add an error message for empty strings (annotation)
    S.nonEmpty({ message: () => "required" }),
    // add a constraint to the schema, only strings with a length less or equal than 10 are valid
    // and add an error message for strings that are too long (annotation)
    S.maxLength(10, { message: (s) => `${s} is too long` }),
    // add an identifier to the schema (annotation)
    S.identifier("Password"),
    // add a title to the schema (annotation)
    S.title("password"),
    // add a description to the schema (annotation)
    S.description(
      "A password is a string of characters used to verify the identity of a user during the authentication process"
    ),
    // add examples to the schema (annotation)
    S.examples(["1Ki77y", "jelly22fi$h"]),
    // add documentation to the schema (annotation)
    S.documentation(`
    jsDoc documentation...
  `)
  );

The example shows some built-in combinators to add meta information, but users can easily add their own meta information by defining a custom combinator.

Here's an example of how to add a deprecated annotation:

import * as AST from "@effect/schema/AST";
import * as S from "@effect/schema/Schema";

const DeprecatedId = Symbol.for(
  "some/unique/identifier/for/the/custom/annotation"
);

const deprecated = <I, A>(self: S.Schema<I, A>): S.Schema<I, A> =>
  S.make(AST.setAnnotation(self.ast, DeprecatedId, true));

const schema = deprecated(S.string);

console.log(schema);
/*
Output:
{
  ast: {
    _tag: 'StringKeyword',
    annotations: {
      [Symbol(@effect/schema/annotation/Title)]: 'string',
      [Symbol(@effect/schema/annotation/Description)]: 'a string',
      [Symbol(some/unique/identifier/for/the/custom/annotation)]: true
    }
  }
  ...
}
*/

Annotations can be read using the getAnnotation helper, here's an example:

import * as Option from "effect/Option";

const isDeprecated = <A>(schema: S.Schema<A>): boolean =>
  AST.getAnnotation<boolean>(DeprecatedId)(schema.ast).pipe(
    Option.getOrElse(() => false)
  );

console.log(isDeprecated(S.string)); // false
console.log(isDeprecated(schema)); // true

Documentation

API Reference

License

The MIT License (MIT)

Contributing Guidelines

Thank you for considering contributing to our project! Here are some guidelines to help you get started:

Reporting Bugs

If you have found a bug, please open an issue on our issue tracker and provide as much detail as possible. This should include:

A clear and concise description of the problem
Steps to reproduce the problem
The expected behavior
The actual behavior
Any relevant error messages or logs

Suggesting Enhancements

If you have an idea for an enhancement or a new feature, please open an issue on our issue tracker and provide as much detail as possible. This should include:

A clear and concise description of the enhancement or feature
Any potential benefits or use cases
Any potential drawbacks or trade-offs

Pull Requests

We welcome contributions via pull requests! Here are some guidelines to help you get started:

Fork the repository and clone it to your local machine.
Create a new branch for your changes: git checkout -b my-new-feature
Ensure you have the required dependencies installed by running: pnpm install (assuming pnpm version 8.x).
Make your desired changes and, if applicable, include tests to validate your modifications.
Run the following commands to ensure the integrity of your changes:
- pnpm check: Verify that the code compiles.
- pnpm test: Execute the tests.
- pnpm circular: Confirm there are no circular imports.
- pnpm lint: Check for code style adherence (if you happen to encounter any errors during this process, you can add the --fix option to automatically fix some of these style issues).
- pnpm dtslint: Run type-level tests.
- pnpm docgen: Update the automatically generated documentation.
Create a changeset for your changes: before committing your changes, create a changeset to document the modifications. This helps in tracking and communicating the changes effectively. To create a changeset, run the following command: pnpm changeset.
Commit your changes: after creating the changeset, commit your changes with a descriptive commit message: git commit -am 'Add some feature'.
Push your changes to your fork: git push origin my-new-feature.
Open a pull request against our main branch.

Pull Request Guidelines

Please make sure your changes are consistent with the project's existing style and conventions.
Please write clear commit messages and include a summary of your changes in the pull request description.
Please make sure all tests pass and add new tests as necessary.
If your change requires documentation, please update the relevant documentation.
Please be patient! We will do our best to review your pull request as soon as possible.

License

By contributing to this project, you agree that your contributions will be licensed under the project's MIT License.

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Package last updated on 19 Dec 2023

@effect/schema

Introduction

Understanding Parsing, Decoding, and Encoding

The Rule of Schemas: Keeping Encode and Decode in Sync

Credits

Requirements

Understanding exactOptionalPropertyTypes

Getting started

Defining a schema

Extracting Inferred Types

Parsing

Handling Async Transformations

Excess properties

All errors

Encoding

Formatting Errors

TreeFormatter (default)

ArrayFormatter

Assertions

fast-check arbitraries

Pretty print

Generating JSON Schemas

Identifier Annotations

Recursive and Mutually Recursive Schemas

JSON Schema Annotations

Generating Equivalences

Basic usage

Cheatsheet

Primitives

Literals

Template literals

Filters

String filters

Number filters

Bigint filters

BigDecimal filters

Duration filters

Array filters

Branded types

Defining a schema from scratch

Reusing an existing branded type

Native enums

Nullables

Unions

Union of literals

Discriminated unions

How to transform a simple union into a discriminated union

Tuples

Append a required element

Append an optional element

Append a rest element

Arrays

Mutable Arrays

Non empty arrays

Structs

Mutable Properties

Optional fields

Default values

Optional fields as Options

Renaming Properties

Classes

The Benefits of Using Classes

Validation and Instantiation

Custom Getters and Methods

Accessing Related Schemas

Tagged Class variants

Extending existing Classes

Transforms

Pick

Omit

Partial

Required

Records

String keys

Keys refinements

Symbol keys

Template literal keys

Mutable Records

Extend

Compose

Understanding `exactOptionalPropertyTypes`

Optional fields as `Option`s

Interop with `effect/Data`