@effect/schema
Advanced tools
Modeling the schema of data structures as first-class values
@effect/schema is a TypeScript library for defining and validating schemas. It provides a way to define the structure of data, validate it, and transform it. The library is designed to be type-safe and integrates well with TypeScript's type system.
Defining Schemas
You can define schemas using the @effect/schema package. In this example, a schema for a user object is defined with 'name' as a string and 'age' as a number.
const { Schema, string, number } = require('@effect/schema');
const userSchema = Schema({
name: string,
age: number
});Validating Data
The @effect/schema package allows you to validate data against a defined schema. In this example, a user object is validated against the userSchema. If the data is valid, it prints the valid user; otherwise, it prints the validation errors.
const { validate } = require('@effect/schema');
const user = { name: 'John Doe', age: 30 };
const result = validate(userSchema, user);
if (result.isValid) {
console.log('Valid user:', result.value);
} else {
console.log('Validation errors:', result.errors);
}Transforming Data
You can also transform data to match the schema. In this example, the age property of the user object is transformed from a string to a number to match the userSchema.
const { transform } = require('@effect/schema');
const user = { name: 'John Doe', age: '30' };
const transformedUser = transform(userSchema, user);
console.log('Transformed user:', transformedUser);Yup is a JavaScript schema builder for value parsing and validation. It is similar to @effect/schema in that it allows you to define schemas and validate data. However, Yup is more widely used and has a larger community.
Joi is a powerful schema description language and data validator for JavaScript. Like @effect/schema, it allows you to define and validate schemas. Joi is known for its extensive feature set and flexibility.
Zod is a TypeScript-first schema declaration and validation library. It is similar to @effect/schema in its focus on TypeScript integration and type safety. Zod is known for its simplicity and ease of use.
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<A, I, R> 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 |
|---|---|
| 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!
sequenceDiagram
participant UA as unknown
participant A
participant I
participant UI as unknown
UI->>A: decodeUnknown
I->>A: decode
A->>I: encode
UA->>I: encodeUnknown
UA->>A: validate
UA->>A: is
UA->>A: asserts
We'll break down these concepts using an example with a Schema<Date, string, never>. 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.
Decoding From Unknown
Decoding from unknown 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 decoding operation, where the data is both validated and transformed.
Encoding From Unknown
Encoding from unknown 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 Date.
Encoding: Following the successful check, we proceed to convert the Date into a string. This process completes the encoding 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.
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.
This library was inspired by the following projects:
strict flag enabled in your tsconfig.json fileexactOptionalPropertyTypes flag enabled in your tsconfig.json file
{
// ...
"compilerOptions": {
// ...
"strict": true,
"exactOptionalPropertyTypes": true
}
}
effect package (peer dependency)exactOptionalPropertyTypesThe @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 S from "@effect/schema/Schema";
/*
const schema: S.Schema<{
readonly myfield?: string; // the type is strict
}, {
readonly myfield?: string; // the type is strict
}, never>
*/
const schema = S.struct({
myfield: S.optional(S.string.pipe(S.nonEmpty()), {
exact: true,
}),
});
S.decodeSync(schema)({ myfield: undefined });
/*
TypeScript Error:
Argument of type '{ myfield: undefined; }' is not assignable to parameter of type '{ readonly myfield?: string; }' with 'exactOptionalPropertyTypes: true'. Consider adding 'undefined' to the types of the target's properties.
Types of property 'myfield' are incompatible.
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 S from "@effect/schema/Schema";
/*
const schema: S.Schema<{
readonly myfield?: string | undefined; // the type is widened to string | undefined
}, {
readonly myfield?: string | undefined; // the type is widened to string | undefined
}, never>
*/
const schema = S.struct({
myfield: S.optional(S.string.pipe(S.nonEmpty()), {
exact: true,
}),
});
S.decodeSync(schema)({ myfield: undefined }); // No type error, but a decoding failure occurs
/*
Error: { myfield?: a non empty string }
└─ ["myfield"]
└─ a non empty string
└─ From side refinement failure
└─ Expected a 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.
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)[!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";
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.
After you've defined a Schema<A, I, R>, 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;
}
*/
Alternatively you can also extract a type instead of an interface:
type Person = S.Schema.To<typeof Person>;
/*
Equivalent to:
type Person {
readonly name: string;
readonly age: number;
}
*/
You can also extract the inferred type R that represents the context described by the schema using the Schema.Context type:
// type Context = never
type Context = S.Schema.Context<typeof Person>;
In cases where I differs from A, you can also extract the inferred I type using Schema.From.
import type * as S from "@effect/schema/Schema";
// type To = number
type To = S.Schema.To<typeof S.NumberFromString>;
// type From = string
type From = S.Schema.From<typeof S.NumberFromString>;
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: S.Schema<Person> = _Person;
Alternatively, you can use Schema.Class (see the Class section below for more details).
Note that the technique shown above becomes more complex when the schema is defined such that A is different from I. For example:
import * as S from "@effect/schema/Schema";
/*
const _Person: S.Schema<{
readonly name: string;
readonly age: number;
}, {
readonly name: string;
readonly age: string;
}, never>
*/
const _Person = S.struct({
name: S.string,
age: S.NumberFromString,
});
interface Person extends S.Schema.To<typeof _Person> {}
interface PersonFrom extends S.Schema.From<typeof _Person> {}
// Re-declare the schema to create a schema with an opaque type
const Person: S.Schema<Person, PersonFrom> = _Person;
In this case, the field "age" is of type string in the From type of the schema and is of type number in the To type of the schema. Therefore, we need to define two interfaces (PersonFrom and Person) and use both to redeclare our final schema Person.
To decode a value from an unknown value using the previously defined Schema, you can make use of the decodeUnknown* functions provided by the @effect/schema/Schema module. Let's explore an example using the decodeUnknownEither 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 parse = S.decodeUnknownEither(Person);
const input: unknown = { name: "Alice", age: 30 };
const result1 = parse(input);
if (Either.isRight(result1)) {
console.log(result1.right);
/*
Output:
{ name: "Alice", age: 30 }
*/
}
const result2 = parse(null);
if (Either.isLeft(result2)) {
console.log(result2.left);
/*
Output:
{
_id: 'ParseError',
message: 'Expected { name: string; age: number }, actual null'
}
*/
}
The parse function returns an Either<ParseError, A>, where ParseError is defined as follows:
interface ParseError {
readonly _tag: "ParseError";
readonly error: ParseIssue;
}
Here, ParseIssue represents an error that might occur during the parsing process. It is wrapped in a tagged error to make it easier to catch errors using Effect.catchTag. The result Either<ParseError, A> contains the inferred data type described by the schema. A successful parse yields a Right value with the parsed data A, while a failed parse results in a Left value containing a ParseError.
Now, let's see another example using the decodeUnknownSync function.
The decodeUnknownSync 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.
import * as S from "@effect/schema/Schema";
const Person = S.struct({
name: S.string,
age: S.number,
});
try {
const person = S.decodeUnknownSync(Person)({});
console.log(person);
} catch (e) {
console.error("Parsing failed:");
console.error(e);
}
/*
Parsing failed:
Error: { name: string; age: number }
└─ ["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.
When your schema involves asynchronous transformations, neither the decodeUnknownSync nor the decodeUnknownEither functions will work for you. In such cases, you must turn to the decodeUnknown function, which returns an Effect.
import * as S from "@effect/schema/Schema";
import * as Effect from "effect/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.decodeUnknownEither(asyncSchema);
console.log(JSON.stringify(syncParsePersonId(1), null, 2));
/*
Output:
{
"_id": "Either",
"_tag": "Left",
"left": {
"_id": "ParseError",
"message": "is forbidden"
}
}
*/
const asyncParsePersonId = S.decodeUnknown(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 decodeUnknownEither 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.
When using a Schema to parse a value, by default 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 set to "error":
import * as S from "@effect/schema/Schema";
const Person = S.struct({
name: S.string,
age: S.number,
});
console.log(
S.decodeUnknownSync(Person)({
name: "Bob",
age: 40,
email: "bob@example.com",
})
);
/*
Output:
{ name: 'Bob', age: 40 }
*/
S.decodeUnknownSync(Person)(
{
name: "Bob",
age: 40,
email: "bob@example.com",
},
{ onExcessProperty: "error" }
);
/*
throws
Error: { name: string; age: number }
└─ ["email"]
└─ is unexpected, expected "name" | "age"
*/
If you want to allow excess properties to remain, you can use onExcessProperty set to "preserve":
import * as S from "@effect/schema/Schema";
const Person = S.struct({
name: S.string,
age: S.number,
});
console.log(
S.decodeUnknownSync(Person)(
{
name: "Bob",
age: 40,
email: "bob@example.com",
},
{ onExcessProperty: "preserve" }
)
);
/*
{ email: 'bob@example.com', name: 'Bob', age: 40 }
*/
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.decodeUnknownSync(Person)(
{
name: "Bob",
age: "abc",
email: "bob@example.com",
},
{ errors: "all", onExcessProperty: "error" }
);
/*
throws
Error: { name: string; age: number }
├─ ["email"]
│ └─ is unexpected, expected "name" | "age"
└─ ["age"]
└─ Expected a number, actual "abc"
*/
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".
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.
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 { formatError } from "@effect/schema/TreeFormatter";
import * as Either from "effect/Either";
const Person = S.struct({
name: S.string,
age: S.number,
});
const result = S.decodeUnknownEither(Person)({});
if (Either.isLeft(result)) {
console.error("Parsing failed:");
console.error(formatError(result.left));
}
/*
Parsing failed:
{ name: string; age: number }
└─ ["name"]
└─ is missing
*/
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:
export interface Issue {
readonly _tag:
| "Transform"
| "Type"
| "Forbidden"
| "Declaration"
| "Refinement"
| "Tuple"
| "TypeLiteral"
| "Missing"
| "Unexpected"
| "Union";
readonly path: ReadonlyArray<PropertyKey>;
readonly message: string;
}
Here's an example of how it works:
import { formatError } 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.decodeUnknownEither(Person)(
{ name: 1, foo: 2 },
{ errors: "all", onExcessProperty: "error" }
);
if (Either.isLeft(result)) {
console.error("Parsing failed:");
console.error(formatError(result.left));
}
/*
Parsing failed:
[
{
_tag: 'Unexpected',
path: [ 'foo' ],
message: 'is unexpected, expected "name" | "age"'
},
{
_tag: 'Type',
path: [ 'name' ],
message: 'Expected a string, actual 1'
},
{ _tag: 'Missing', path: [ 'age' ], message: 'is missing' }
]
*/
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: (a: unknown, options?: ParseOptions | undefined) => a is {
readonly name: string;
readonly age: number;
}
*/
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: { name: string; age: number }
└─ ["age"]
└─ Expected a number, actual "30"
*/
// this will not throw an error
assertsPerson({ name: "Alice", age: 30 });
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.compose(S.NumberFromString), S.int()),
});
/*
fc.Arbitrary<{
readonly name: string;
readonly age: number;
}>
*/
const PersonArbitraryTo = Arbitrary.make(Person)(fc);
console.log(fc.sample(PersonArbitraryTo, 2));
/*
Output:
[ { name: 'iP=!', age: -6 }, { name: '', age: 14 } ]
*/
/*
Arbitrary for the "From" type:
fc.Arbitrary<{
readonly name: string;
readonly age: string;
}>
*/
const PersonArbitraryFrom = Arbitrary.make(S.from(Person))(fc);
console.log(fc.sample(PersonArbitraryFrom, 2));
/*
Output:
[ { name: '{F', age: '$"{|' }, { name: 'nB}@BK', age: '^V+|W!Z' } ]
*/
"type": "module" in package.jsonIf you have set the "type" field in your package.json to "module", you might encounter the following error:
import * as S from "@effect/schema/Schema";
import * as Arbitrary from "@effect/schema/Arbitrary";
import * as fc from "fast-check";
const arb = Arbitrary.make(S.string)(fc);
/*
...more lines...
Types have separate declarations of a private property 'internalRng'.
*/
To address this issue, you can apply a patch, for example using pnpm patch, to the fast-check package in the node_modules directory:
diff --git a/CHANGELOG.md b/CHANGELOG.md
deleted file mode 100644
index 41d6274a9d4bb2d9924fb82f77e502f232fd12f5..0000000000000000000000000000000000000000
diff --git a/package.json b/package.json
index e871dfde5f8877b1b7de9bd3d9a6e3e4f7f59843..819035d70e22d246c615bda25183db9b5e124287 100644
--- a/package.json
+++ b/package.json
@@ -12,7 +12,7 @@
"default": "./lib/fast-check.js"
},
"import": {
- "types": "./lib/esm/types/fast-check.d.ts",
+ "types": "./lib/types/fast-check.d.ts",
"default": "./lib/esm/fast-check.js"
}
}
This patch helps resolve the issue caused by the declaration of a private property 'internalRng' having separate declarations in the types when using "type": "module" in package.json.
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.make(Person);
// returns a string representation of the object
console.log(PersonPretty({ name: "Alice", age: 30 }));
/*
Output:
'{ "name": "Alice", "age": 30 }'
*/
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.make(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.
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.make(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 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.make(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.
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 S from "@effect/schema/Schema";
// Simulate one or more refinements
const Positive = S.number.pipe(
S.filter((n) => n > 0, {
jsonSchema: { minimum: 0 },
})
);
const schema = Positive.pipe(
S.filter((n) => n <= 10, {
jsonSchema: { maximum: 10 },
})
);
console.log(JSONSchema.make(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 S.number. This approach helps handle multiple refinements while maintaining clarity in your code.
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,
});
// $ExpectType Equivalence<{ readonly name: string; readonly age: number; }>
const PersonEquivalence = Equivalence.make(Person);
const john = { name: "John", age: 23 };
const alice = { name: "Alice", age: 30 };
console.log(PersonEquivalence(john, { name: "John", age: 23 })); // Output: true
console.log(PersonEquivalence(john, alice)); // Output: false
| Typescript Type | Description / Notes | Schema / Combinator |
|---|---|---|
null | S.null | |
undefined | S.undefined | |
string | S.string | |
number | S.number | |
boolean | S.boolean | |
symbol | S.symbolFromSelf / S.symbol | |
bigint | S.bigintFromSelf / S.bigint | |
unknown | S.unknown | |
any | S.any | |
never | S.never | |
object | S.object | |
unique symbol | S.uniqueSymbol | |
"a", 1, true | type literals | S.literal("a"), S.literal(1), S.literal(true) |
a${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, { exact: true }) }) |
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) |
import * as S from "@effect/schema/Schema";
// primitive values
S.string;
S.number;
S.bigint; // Schema<bigint, string>
S.boolean;
S.symbol; // Schema<symbol, string>
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.UUID;
S.ULID;
import * as S from "@effect/schema/Schema";
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);
The templateLiteral combinator allows you to create a schema for a TypeScript template literal type.
import * as S from "@effect/schema/Schema";
// 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");
// Schema<"welcome_email_id" | "email_heading_id" | "footer_title_id" | "footer_sendoff_id">
S.templateLiteral(S.union(EmailLocaleIDs, FooterLocaleIDs), S.literal("_id"));
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.decodeUnknownSync(LongString)("a"));
/*
throws:
Error: a string at least 10 characters long
...stack...
*/
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:
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",
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";
import * as Option from "effect/Option";
const schema = S.struct({ a: S.string, b: S.string }).pipe(
S.filter((o) =>
o.b === o.a
? Option.none()
: Option.some(
ParseResult.type(
S.literal(o.a).ast,
o.b,
`b ("${o.b}") should be equal to a ("${o.a}")`
)
)
)
);
console.log(S.decodeUnknownSync(schema)({ a: "foo", b: "bar" }));
/*
throws:
Error: <refinement schema>
└─ Predicate refinement failure
└─ b ("bar") should be equal to a ("foo")
*/
[!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.
import * as S from "@effect/schema/Schema";
S.string.pipe(S.maxLength(5));
S.string.pipe(S.minLength(5));
S.NonEmpty; // same as S.string.pipe(S.maxLength(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.
import * as S from "@effect/schema/Schema";
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
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
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());
import * as S from "@effect/schema/Schema";
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"));
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
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:
effect/Brand and want to reuse it to define a schemaTo 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 * 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 * 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>
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 * as S from "@effect/schema/Schema";
// Define a schema for the branded type
const UserIdSchema = S.string.pipe(S.fromBrand(UserId));
import * as S from "@effect/schema/Schema";
enum Fruits {
Apple,
Banana,
}
// Schema<Fruits>
S.enums(Fruits);
import * as S from "@effect/schema/Schema";
// Schema<string | null>
S.nullable(S.string);
// Schema<string | null | undefined>
S.nullish(S.string);
@effect/schema/Schema includes a built-in union combinator for composing "OR" types.
import * as S from "@effect/schema/Schema";
// Schema<string | number>
S.union(S.string, S.number);
While the following is perfectly acceptable:
import * as S from "@effect/schema/Schema";
// 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:
import * as S from "@effect/schema/Schema";
// 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.
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.
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";
import * as assert from "node:assert";
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
)
)
);
assert.deepStrictEqual(
S.decodeUnknownSync(DiscriminatedShape)({ radius: 10 }),
{
kind: "circle",
radius: 10,
}
);
assert.deepStrictEqual(
S.decodeUnknownSync(DiscriminatedShape)({ sideLength: 10 }),
{
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:
import * as S from "@effect/schema/Schema";
import * as assert from "node:assert";
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"))
);
// decoding
assert.deepStrictEqual(
S.decodeUnknownSync(DiscriminatedShape)({ radius: 10 }),
{
kind: "circle",
radius: 10,
}
);
// encoding
assert.deepStrictEqual(
S.encodeSync(DiscriminatedShape)({
kind: "circle",
radius: 10,
}),
{ radius: 10 }
);
import * as S from "@effect/schema/Schema";
// Schema<readonly [string, number]>
S.tuple(S.string, S.number);
import * as S from "@effect/schema/Schema";
// Schema<readonly [string, number, boolean]>
S.tuple(S.string, S.number).pipe(S.element(S.boolean));
import * as S from "@effect/schema/Schema";
// Schema<readonly [string, number, boolean?]>
S.tuple(S.string, S.number).pipe(S.optionalElement(S.boolean));
import * as S from "@effect/schema/Schema";
// Schema<readonly [string, number, ...boolean[]]>
S.tuple(S.string, S.number).pipe(S.rest(S.boolean));
import * as S from "@effect/schema/Schema";
// Schema<readonly number[]>
S.array(S.number);
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:
import * as S from "@effect/schema/Schema";
// Schema<number[]>
S.mutable(S.array(S.number));
import * as S from "@effect/schema/Schema";
// Schema<readonly [number, ...number[]]>
S.nonEmptyArray(S.number);
import * as S from "@effect/schema/Schema";
// Schema<{ readonly a: string; readonly b: number; }>
S.struct({ a: S.string, b: S.number });
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:
import * as S from "@effect/schema/Schema";
// Schema<{ a: string; b: number; }>
S.mutable(S.struct({ a: S.string, b: S.number }));
Cheatsheet
| Combinator | From | To |
|---|---|---|
optional | Schema<A, I, R> | PropertySignature<I | undefined, true, A | undefined, true, R> |
optional | Schema<A, I, R>, { exact: true } | PropertySignature<I, true, A, true, R> |
<missing value> -> <missing value>undefined -> undefinedi -> a<missing value> -> <missing value>undefined -> undefineda -> i<missing value> -> <missing value>i -> a<missing value> -> <missing value>a -> i| Combinator | From | To |
|---|---|---|
optional | Schema<A, I, R>, { default: () => A } | PropertySignature<I | undefined, true, A, false, R> |
optional | Schema<A, I, R>, { exact: true, default: () => A } | PropertySignature<I, true, A, false, R> |
optional | Schema<A, I, R>, { nullable: true, default: () => A } | PropertySignature<I | null | undefined, true, A, false, R> |
optional | Schema<A, I, R>, { exact: true, nullable: true, default: () => A } | PropertySignature<I | null, true, A, false, R> |
<missing value> -> <default value>undefined -> <default value>i -> aa -> i<missing value> -> <default value>i -> aa -> i<missing value> -> <default value>undefined -> <default value>null -> <default value>i -> aa -> i<missing value> -> <default value>null -> <default value>i -> aa -> iOptions| Combinator | From | To |
|---|---|---|
optional | Schema<A, I, R>, { as: "Option" } | PropertySignature<I | undefined, true, Option<A>, false, R> |
optional | Schema<A, I, R>, { exact: true, as: "Option" } | PropertySignature<I, true, Option<A>, false, R> |
optional | Schema<A, I, R>, { nullable: true, as: "Option" } | PropertySignature<I | undefined | null, true, Option<A>, false, R> |
optional | Schema<A, I, R>, { exact: true, nullable: true, as: "Option" } | PropertySignature<I | null, true, Option<A>, false, R> |
<missing value> -> Option.none()undefined -> Option.none()i -> Option.some(a)Option.none() -> <missing value>Option.some(a) -> i<missing value> -> Option.none()i -> Option.some(a)Option.none() -> <missing value>Option.some(a) -> i<missing value> -> Option.none()undefined -> Option.none()null -> Option.none()i -> Option.some(a)Option.none() -> <missing value>Option.some(a) -> i<missing value> -> Option.none()null -> Option.none()i -> Option.some(a)Option.none() -> <missing value>Option.some(a) -> iTo rename one or more properties, you can utilize the rename API:
import * as S from "@effect/schema/Schema";
// Original Schema
const originalSchema = S.struct({ a: S.string, b: S.number });
// Renaming the "a" property to "c"
const renamedSchema = S.rename(originalSchema, { a: "c" });
console.log(S.decodeUnknownSync(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 S.decodeUnknownSync, transforms the input object by renaming the specified property.
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.
Classes offer several features that simplify the schema creation process:
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()),
}) {}
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: { id: number; name: a non empty string }
└─ ["name"]
└─ a non empty string
└─ Predicate refinement failure
└─ Expected a non empty string, actual ""
*/
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.
import * as S from "@effect/schema/Schema";
class Person extends S.Class<Person>()({
id: S.number,
name: S.string.pipe(S.nonEmpty()),
}) {}
console.log(S.isSchema(Person)); // true
// Schema<{ readonly id: number; name: string; }>
Person.struct;
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
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;
}
}
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.decodeUnknownSync(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.transformOrFail<PersonWithTransform>()(
{
age: S.optional(S.number, { exact: true, as: "Option" }),
},
(input) =>
Effect.mapBoth(getAge(input.id), {
onFailure: (e) => ParseResult.type(S.string.ast, input.id, e.message),
// must return { age: Option<number> }
onSuccess: (age) => ({ ...input, age: Option.some(age) }),
}),
ParseResult.succeed
) {}
S.decodeUnknownPromise(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.transformOrFailFrom<PersonWithTransformFrom>()(
{
age: S.optional(S.number, { exact: true, as: "Option" }),
},
(input) =>
Effect.mapBoth(getAge(input.id), {
onFailure: (e) => ParseResult.type(S.string.ast, input, e.message),
// must return { age?: number }
onSuccess: (age) => (age > 18 ? { ...input, age } : { ...input }),
}),
ParseResult.succeed
) {}
S.decodeUnknownPromise(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:
{ age: Option<number> }.Using transformFrom:
{ age?: number }.{ age: S.optionalToOption(S.number, { exact: true }) } is executed.The pick operation is used to select specific properties from a schema.
import * as S from "@effect/schema/Schema";
// Schema<{ readonly a: string; }>
S.struct({ a: S.string, b: S.number, c: S.boolean }).pipe(S.pick("a"));
// Schema<{ readonly a: string; readonly c: boolean; }>
S.struct({ a: S.string, b: S.number, c: S.boolean }).pipe(S.pick("a", "c"));
The omit operation is employed to exclude certain properties from a schema.
import * as S from "@effect/schema/Schema";
// Schema<{ readonly b: number; readonly c: boolean; }>
S.struct({ a: S.string, b: S.number, c: S.boolean }).pipe(S.omit("a"));
// Schema<{ readonly b: number; }>
S.struct({ a: S.string, b: S.number, c: S.boolean }).pipe(S.omit("a", "c"));
The partial operation makes all properties within a schema optional.
import * as S from "@effect/schema/Schema";
// Schema<{ readonly a?: string; readonly b?: number; }>
S.partial(S.struct({ a: S.string, b: S.number }));
The required operation ensures that all properties in a schema are mandatory.
import * as S from "@effect/schema/Schema";
// Schema<{ readonly a: string; readonly b: number; }>
S.required(
S.struct({
a: S.optional(S.string, { exact: true }),
b: S.optional(S.number, { exact: true }),
})
);
import * as S from "@effect/schema/Schema";
// Schema<{ readonly [x: string]: string; }>
S.record(S.string, S.string);
// Schema<{ readonly a: string; readonly b: string; }>
S.record(S.union(S.literal("a"), S.literal("b")), S.string);
import * as S from "@effect/schema/Schema";
// Schema<{ readonly [x: string]: string; }>
S.record(S.string.pipe(S.minLength(2)), S.string);
import * as S from "@effect/schema/Schema";
// Schema<{ readonly [x: symbol]: string; }>
S.record(S.symbolFromSelf, S.string);
import * as S from "@effect/schema/Schema";
// Schema<{ readonly [x: `a${string}`]: string; }>
S.record(S.templateLiteral(S.literal("a"), S.string), S.string);
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:
import * as S from "@effect/schema/Schema";
// Schema<{ [x: string]: string; }>
S.mutable(S.record(S.string, S.string););
The extend combinator allows you to add additional fields or index signatures to an existing Schema.
import * as S from "@effect/schema/Schema";
// 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
);
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<B, A, R1> and Schema<C, B, R2>, into a single schema Schema<C, A, R1 | R2>:
import * as S from "@effect/schema/Schema";
// Schema<readonly string[], string>
const schema1 = S.split(",");
// Schema<readonly number[], readonly string[]>
const schema2 = S.array(S.NumberFromString);
// Schema<readonly number[], string>
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.
If you need to be less restrictive when composing your schemas, i.e., when you have something like Schema<R1, A, B> and Schema<R2, C, D> where C is different from B, you can make use of the { strict: false } option:
declare const compose: <A, B, R1, D, C, R2>(
from: Schema<B, A, R1>,
to: Schema<D, C, R2>, // Less strict constraint
options: { strict: false }
) => Schema<D, A, R1 | R2>;
This is useful when you want to relax the type constraints imposed by the decode and encode functions, making them more permissive:
import * as S from "@effect/schema/Schema";
// error: Type 'string | null' is not assignable to type 'string'
S.compose(S.union(S.null, S.string), S.NumberFromString);
// ok
S.compose(S.union(S.null, S.string), S.NumberFromString, { strict: false });
In the following section, we demonstrate how to use the instanceOf combinator to create a Schema for a class instance.
import * as S from "@effect/schema/Schema";
class Test {
constructor(readonly name: string) {}
}
// Schema<Test>
S.instanceOf(Test);
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,
});
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.
declare const transform: <B, A, R1, D, C, R2>(
from: Schema<B, A, R1>,
to: Schema<D, C, R2>,
decode: (b: B) => C,
encode: (c: C) => B
) => Schema<D, A, R1 | R2>;
flowchart TD
schema1["from: Schema<B, A>"]
schema2["to: Schema<D, C>"]
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<readonly [string], 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].
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: <B, A, R1, D, C, R2>(
from: Schema<B, A, R1>,
to: Schema<D, C, R2>,
decode: (b: B) => unknown, // Less strict constraint
encode: (c: C) => unknown, // Less strict constraint
options: { strict: false }
) => Schema<D, A, R1 | R2>;
This is useful when you want to relax the type constraints imposed by the decode and encode functions, making them more permissive.
The transformOrFail combinator works in a similar way, but allows the transformation function to return an Effect<R3, ParseError, A, 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<boolean, string> = 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.Effect<never, Error, unknown> =>
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/${s}`), {
onFailure: (e) => ParseResult.type(ast, s, e.message),
onSuccess: () => s,
}),
ParseResult.succeed
);
const parse = (id: string) =>
Effect.mapError(S.decodeUnknown(PeopleIdFromString)(id), (e) =>
TreeFormatter.formatError(e)
);
Effect.runPromiseExit(parse("1")).then(console.log);
/*
Output:
{ _id: 'Exit', _tag: 'Success', value: '1' }
*/
Effect.runPromiseExit(parse("fail")).then(console.log);
/*
Output:
{
_id: 'Exit',
_tag: 'Failure',
cause: {
_id: 'Cause',
_tag: 'Fail',
failure: '(string <-> string)\n└─ Transformation process failure\n └─ Error: 404'
}
}
*/
You can also declare dependencies:
import * as ParseResult from "@effect/schema/ParseResult";
import * as S from "@effect/schema/Schema";
import * as TreeFormatter from "@effect/schema/TreeFormatter";
import * as Context from "effect/Context";
import * as Effect from "effect/Effect";
import * as Layer from "effect/Layer";
const Fetch = Context.GenericTag<"Fetch", typeof fetch>();
const api = (url: string): Effect.Effect<"Fetch", Error, unknown> =>
Fetch.pipe(
Effect.flatMap((fetch) =>
Effect.tryPromise({
try: () =>
fetch(url).then((res) => {
if (res.ok) {
return res.json() as Promise<unknown>;
}
throw new Error(String(res.status));
}),
catch: (e) => new Error(String(e)),
})
)
);
const PeopleId = S.string.pipe(S.brand("PeopleId"));
const PeopleIdFromString = S.transformOrFail(
S.string,
PeopleId,
(s, _, ast) =>
Effect.mapBoth(api(`https://swapi.dev/api/people/${s}`), {
onFailure: (e) => ParseResult.type(ast, s, e.message),
onSuccess: () => s,
}),
ParseResult.succeed
);
const parse = (id: string) =>
Effect.mapError(S.decodeUnknown(PeopleIdFromString)(id), (e) =>
TreeFormatter.formatError(e)
);
const FetchLive = Layer.succeed(Fetch, fetch);
Effect.runPromiseExit(parse("1").pipe(Effect.provide(FetchLive))).then(
console.log
);
/*
Output:
{ _id: 'Exit', _tag: 'Success', value: '1' }
*/
Effect.runPromiseExit(parse("fail").pipe(Effect.provide(FetchLive))).then(
console.log
);
/*
Output:
{
_id: 'Exit',
_tag: 'Failure',
cause: {
_id: 'Cause',
_tag: 'Fail',
failure: '(string <-> string)\n└─ Transformation process failure\n └─ Error: 404'
}
}
*/
The split combinator allows splitting a string into an array of strings.
import * as S from "@effect/schema/Schema";
// Schema<string[], string>
const schema = S.split(",");
const parse = S.decodeUnknownSync(schema);
console.log(parse("")); // [""]
console.log(parse(",")); // ["", ""]
console.log(parse("a,")); // ["a", ""]
console.log(parse("a,b")); // ["a", "b"]
The Trim schema allows removing whitespaces from the beginning and end of a string.
import * as S from "@effect/schema/Schema";
// Schema<string>
const schema = S.Trim;
const parse = S.decodeUnknownSync(schema);
console.log(parse("a")); // "a"
console.log(parse(" a")); // "a"
console.log(parse("a ")); // "a"
console.log(parse(" a ")); // "a"
Note. If you were looking for a combinator to check if a string is trimmed, check out the trimmed combinator.
The Lowercase schema converts a string to lowercase.
import * as S from "@effect/schema/Schema";
// S.Schema<string>
const schema = S.Lowercase;
const parse = S.decodeUnknownSync(schema);
console.log(parse("A")); // "a"
console.log(parse(" AB")); // " ab"
console.log(parse("Ab ")); // "ab "
console.log(parse(" ABc ")); // " abc "
Note. If you were looking for a combinator to check if a string is lowercased, check out the lowercased combinator.
The parseJson constructor 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";
// Schema<unknown, string>
const schema = S.parseJson();
const parse = S.decodeUnknownSync(schema);
// Parse valid JSON strings
console.log(parse("{}")); // Output: {}
console.log(parse(`{"a":"b"}`)); // Output: { a: "b" }
// Attempting to parse an empty string results in an error
parse("");
/*
throws:
Error: (JsonString <-> unknown)
└─ Transformation process failure
└─ Unexpected end of JSON input
*/
Additionally, you can refine the parsing result by providing a schema to the parseJson constructor:
import * as S from "@effect/schema/Schema";
// Schema<{ readonly a: number; }, string>
const schema = S.parseJson(S.struct({ a: S.number }));
In this example, we've used parseJson with a struct schema to ensure that the parsed result has a specific structure, including an object with a numeric property "a". This helps in handling JSON data with predefined shapes.
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";
// Schema<number, string>
const schema = S.NumberFromString;
const parse = S.decodeUnknownSync(schema);
// success cases
console.log(parse("1")); // 1
console.log(parse("-1")); // -1
console.log(parse("1.5")); // 1.5
console.log(parse("NaN")); // NaN
console.log(parse("Infinity")); // Infinity
console.log(parse("-Infinity")); // -Infinity
// failure cases
parse("a");
/*
throws:
Error: NumberFromString
└─ Transformation process failure
└─ Expected NumberFromString, actual "a"
*/
Clamps a number between a minimum and a maximum value.
import * as S from "@effect/schema/Schema";
// Schema<number>
const schema = S.number.pipe(S.clamp(-1, 1)); // clamps the input to -1 <= x <= 1
const parse = S.decodeUnknownSync(schema);
console.log(parse(-3)); // -1
console.log(parse(0)); // 0
console.log(parse(3)); // 1
Transforms a string into a BigDecimal.
import * as S from "@effect/schema/Schema";
// Schema<BigDecimal, string>
const schema = S.BigDecimal;
const parse = S.decodeUnknownSync(schema);
console.log(parse(".124")); // { _id: 'BigDecimal', value: '124', scale: 3 }
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";
// Schema<BigDecimal, number>
const schema = S.BigDecimalFromNumber;
const parse = S.decodeUnknownSync(schema);
console.log(parse(0.111)); // { _id: 'BigDecimal', value: '111', scale: 3 }
Clamps a BigDecimal between a minimum and a maximum value.
import * as S from "@effect/schema/Schema";
import * as BigDecimal from "effect/BigDecimal";
// Schema<BigDecimal, string>
const schema = S.BigDecimal.pipe(
S.clampBigDecimal(BigDecimal.fromNumber(-1), BigDecimal.fromNumber(1))
);
const parse = S.decodeUnknownSync(schema);
console.log(parse("-2")); // { _id: 'BigDecimal', value: '-1', scale: 0 }
console.log(parse("0")); // { _id: 'BigDecimal', value: '0', scale: 0 }
console.log(parse("3")); // { _id: 'BigDecimal', value: '1', scale: 0 }
Negates a BigDecimal.
import * as S from "@effect/schema/Schema";
// Schema<BigDecimal, string>
const schema = S.BigDecimal.pipe(S.negateBigDecimal);
const parse = S.decodeUnknownSync(schema);
console.log(parse("-2")); // { _id: 'BigDecimal', value: '2', scale: 0 }
console.log(parse("0")); // { _id: 'BigDecimal', value: '0', scale: 0 }
console.log(parse("3")); // { _id: 'BigDecimal', value: '-3', scale: 0 }
Converts an hrtime(i.e. [seconds: number, nanos: number]) into a Duration.
import * as S from "@effect/schema/Schema";
// Schema<Duration, number>
const schema = S.Duration;
const parse = S.decodeUnknownSync(schema);
console.log(parse([0, 0])); // { _id: 'Duration', _tag: 'Nanos', hrtime: [ 0, 0 ] }
console.log(parse([5000, 0])); // { _id: 'Duration', _tag: 'Nanos', hrtime: [ 5000, 0 ] }
Converts a number into a Duration where the number represents the number of milliseconds.
import * as S from "@effect/schema/Schema";
// Schema<Duration, number>
const schema = S.DurationFromMillis;
const parse = S.decodeUnknownSync(schema);
console.log(parse(0)); // { _id: 'Duration', _tag: 'Millis', millis: 0 }
console.log(parse(5000)); // { _id: 'Duration', _tag: 'Millis', millis: 5000 }
Converts a bigint into a Duration where the number represents the number of nanoseconds.
import * as S from "@effect/schema/Schema";
// Schema<Duration, bigint>
const schema = S.DurationFromNanos;
const parse = S.decodeUnknownSync(schema);
console.log(parse(0n)); // { _id: 'Duration', _tag: 'Nanos', hrtime: [ 0, 0 ] }
console.log(parse(5000000000n)); // { _id: 'Duration', _tag: 'Nanos', hrtime: [ 5, 0 ] }
Clamps a Duration between a minimum and a maximum value.
import * as S from "@effect/schema/Schema";
import * as Duration from "effect/Duration";
// Schema<Duration>
const schema = S.DurationFromSelf.pipe(
S.clampDuration("5 seconds", "10 seconds")
);
const parse = S.decodeUnknownSync(schema);
console.log(parse(Duration.decode("2 seconds"))); // { _id: 'Duration', _tag: 'Millis', millis: 5000 }
console.log(parse(Duration.decode("6 seconds"))); // { _id: 'Duration', _tag: 'Millis', millis: 6000 }
console.log(parse(Duration.decode("11 seconds"))); // { _id: 'Duration', _tag: 'Millis', millis: 10000 }
Converts a string into a Secret.
import * as S from "@effect/schema/Schema";
// Schema<Secret, string>
const schema = S.Secret;
const parse = S.decodeUnknownSync(schema);
console.log(parse("keep it secret, keep it safe")); // {}
Transforms a string into a symbol by parsing the string using Symbol.for.
import * as S from "@effect/schema/Schema";
// Schema<symbol, string>
const schema = S.symbol;
const parse = S.decodeUnknownSync(schema);
console.log(parse("a")); // Symbol(a)
Transforms a string into a bigint by parsing the string using BigInt.
import * as S from "@effect/schema/Schema";
// Schema<bigint, string>
const schema = S.bigint;
const parse = S.decodeUnknownSync(schema);
// success cases
console.log(parse("1")); // 1n
console.log(parse("-1")); // -1n
// failure cases
parse("a");
/*
throws:
Error: bigint
└─ Transformation process failure
└─ Expected bigint, actual "a"
*/
parse("1.5"); // throws
parse("NaN"); // throws
parse("Infinity"); // throws
parse("-Infinity"); // throws
Transforms a number into a bigint by parsing the number using BigInt.
import * as S from "@effect/schema/Schema";
// Schema<bigint, number>
const schema = S.BigintFromNumber;
const parse = S.decodeUnknownSync(schema);
const encode = S.encodeSync(schema);
// success cases
console.log(parse(1)); // 1n
console.log(parse(-1)); // -1n
console.log(encode(1n)); // 1
console.log(encode(-1n)); // -1
// failure cases
parse(1.5);
/*
throws:
Error: BigintFromNumber
└─ Transformation process failure
└─ Expected BigintFromNumber, actual 1.5
*/
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
Clamps a bigint between a minimum and a maximum value.
import * as S from "@effect/schema/Schema";
// Schema<bigint>
const schema = S.bigintFromSelf.pipe(S.clampBigint(-1n, 1n)); // clamps the input to -1n <= x <= 1n
const parse = S.decodeUnknownSync(schema);
console.log(parse(-3n)); // -1n
console.log(parse(0n)); // 0n
console.log(parse(3n)); // 1n
Negates a boolean value.
import * as S from "@effect/schema/Schema";
// Schema<boolean>
const schema = S.Not;
const parse = S.decodeUnknownSync(schema);
console.log(parse(true)); // false
console.log(parse(false)); // true
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";
// Schema<Date, string>
const schema = S.Date;
const parse = S.decodeUnknownSync(schema);
console.log(parse("1970-01-01T00:00:00.000Z")); // 1970-01-01T00:00:00.000Z
parse("a");
/*
throws:
Error: Date
└─ Predicate refinement failure
└─ Expected Date (a valid Date), actual Invalid Date
*/
const validate = S.validateSync(schema);
console.log(validate(new Date(0))); // 1970-01-01T00:00:00.000Z
validate(new Date("Invalid Date"));
/*
throws:
Error: Date
└─ Predicate refinement failure
└─ Expected Date (a valid Date), actual Invalid Date
*/
effect/DataThe 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>:
import * as S from "@effect/schema/Schema";
import * as Equal from "effect/Equal";
/*
Schema<Data.Data<{
readonly name: string;
readonly age: number;
}>, {
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
Cheatsheet
| Combinator | From | To |
|---|---|---|
option | Schema<A, I, R> | Schema<Option<A>, OptionFrom<I>, R> |
optionFromSelf | Schema<A, I, R> | Schema<Option<A>, Option<I>, R> |
optionFromNullable | Schema<A, I, R> | Schema<Option<A>, I | null, R> |
optionFromNullish | Schema<A, I, R>, null | undefined | Schema<Option<A>, I | null | undefined, R> |
where
type OptionFrom<I> =
| {
readonly _tag: "None";
}
| {
readonly _tag: "Some";
readonly value: I;
};
{ _tag: "None" } -> Option.none(){ _tag: "Some", value: i } -> Option.some(a)Option.none() -> { _tag: "None" }Option.some(a) -> { _tag: "Some", value: i }Option.none() -> Option.none()Option.some(i) -> Option.some(a)Option.none() -> Option.none()Option.some(a) -> Option.some(i)null -> Option.none()i -> Option.some(a)Option.none() -> nullOption.some(a) -> inull -> Option.none()undefined -> Option.none()i -> Option.some(a)Option.none() -> <onNoneEncoding value>Option.some(a) -> iIn 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";
// Schema<ReadonlySet<number>, readonly number[]>
const schema = S.readonlySet(S.number); // define a schema for ReadonlySet with number values
const parse = S.decodeUnknownSync(schema);
console.log(parse([1, 2, 3])); // Set(3) { 1, 2, 3 }
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";
// Schema<ReadonlyMap<number, string>, readonly (readonly [number, string])[]>
const schema = S.readonlyMap(S.number, S.string); // define the schema for ReadonlyMap with number keys and string values
const parse = S.decodeUnknownSync(schema);
console.log(
parse([
[1, "a"],
[2, "b"],
[3, "c"],
])
); // Map(3) { 1 => 'a', 2 => 'b', 3 => 'c' }
Creating schemas for new data types is crucial to defining the expected structure of information in your application. This guide explores how to declare schemas for new data types. We'll cover two important concepts: declaring schemas for primitive data types and type constructors.
A primitive data type represents simple values. To declare a schema for a primitive data type, like the File type in TypeScript, we use the S.declare constructor along with a type guard. Let's go through an example:
import * as S from "@effect/schema/Schema";
const isFile = (input: unknown): input is File => input instanceof File;
// const FileFromSelf: S.Schema<File>
const FileFromSelf = S.declare(isFile, {
identifier: "FileFromSelf",
});
const parseSync = S.decodeUnknownSync(FileFromSelf);
console.log(parseSync(new File([], ""))); // File { size: 0, type: '', name: '', lastModified: 1705595977234 }
parseSync(null);
/*
throws
Error: Error: Expected FileFromSelf, actual null
*/
Type constructors are generic types that take one or more types as arguments and return a new type. If you need to define a schema for a type constructor, you can use the S.declare constructor. Let's illustrate this with a schema for ReadonlySet<A>:
import * as ParseResult from "@effect/schema/ParseResult";
import * as S from "@effect/schema/Schema";
export const myReadonlySet = <A, I, R>(
// Schema for the elements of the Set
item: S.Schema<A, I, R>
): S.Schema<ReadonlySet<A>, ReadonlySet<I>, R> =>
S.declare(
// Store the schema for the elements
[item],
// Decoding function
(item) => (input, parseOptions, ast) => {
if (input instanceof Set) {
// Decode the elements
const elements = ParseResult.decodeUnknown(S.array(item))(
Array.from(input.values()),
parseOptions
);
// Return a Set containing the parsed elements
return ParseResult.map(elements, (as): ReadonlySet<A> => new Set(as));
}
return ParseResult.fail(ParseResult.type(ast, input));
},
// Encoding function
(item) => (input, parseOptions, ast) => {
if (input instanceof Set) {
// Encode the elements
const elements = ParseResult.encodeUnknown(S.array(item))(
Array.from(input.values()),
parseOptions
);
// Return a Set containing the parsed elements
return ParseResult.map(elements, (is): ReadonlySet<I> => new Set(is));
}
return ParseResult.fail(ParseResult.type(ast, input));
},
{
description: `ReadonlySet<${S.format(item)}>`,
}
);
// const setOfNumbers: S.Schema<ReadonlySet<string>, ReadonlySet<number>>
const setOfNumbers = myReadonlySet(S.NumberFromString);
const parseSync = S.decodeUnknownSync(setOfNumbers);
console.log(parseSync(new Set(["1", "2", "3"]))); // Set(3) { 1, 2, 3 }
parseSync(null);
/*
throws
Error: Expected ReadonlySet<NumberFromString>, actual null
*/
parseSync(new Set(["1", null, "3"]));
/*
throws
Error: ReadonlySet<NumberFromString>
└─ ReadonlyArray<NumberFromString>
└─ [1]
└─ NumberFromString
└─ From side transformation failure
└─ Expected a string, actual null
*/
When you define a new data type, some compilers like Arbitrary or Pretty may not know how to handle the newly defined data. For instance:
import * as Arbitrary from "@effect/schema/Arbitrary";
import * as S from "@effect/schema/Schema";
const isFile = (input: unknown): input is File => input instanceof File;
const FileFromSelf = S.declare(isFile, {
identifier: "FileFromSelf",
});
// Create an Arbitrary instance for FileFromSelf schema
const arb = Arbitrary.make(FileFromSelf);
/*
throws:
Error: cannot build an Arbitrary for a declaration without annotations (FileFromSelf)
*/
In such cases, you need to provide annotations to ensure proper functionality:
import * as Arbitrary from "@effect/schema/Arbitrary";
import * as Pretty from "@effect/schema/Pretty";
import * as S from "@effect/schema/Schema";
import * as fc from "fast-check";
const isFile = (input: unknown): input is File => input instanceof File;
const FileFromSelf = S.declare(isFile, {
identifier: "FileFromSelf",
// Provide an arbitrary function to generate random File instances
arbitrary: () => (fc) =>
fc
.tuple(fc.string(), fc.string())
.map(([path, content]) => new File([content], path)),
// Provide a pretty function to generate human-readable representation of File instances
pretty: () => (file) => `File(${file.name})`,
});
// Create an Arbitrary instance for FileFromSelf schema
const arb = Arbitrary.make(FileFromSelf);
// Generate sample files using the Arbitrary instance
const files = fc.sample(arb(fc), 2);
console.log(files);
/*
Output:
[
File { size: 5, type: '', name: 'C', lastModified: 1706435571176 },
File { size: 1, type: '', name: '98Ggmc', lastModified: 1706435571176 }
]
*/
// Create a Pretty instance for FileFromSelf schema
const pretty = Pretty.make(FileFromSelf);
// Print human-readable representation of a file
console.log(pretty(files[0])); // "File(C)"
Since there are various different definitions of what constitutes a valid email address depending on the environment and use case, @effect/schema does not provide a built-in combinator for parsing email addresses. However, it is easy to define a custom combinator that can be used to parse email addresses.
import * as S from "@effect/schema/Schema";
// see https://stackoverflow.com/questions/46155/how-can-i-validate-an-email-address-in-javascript/46181#46181
const Email = S.pattern(
/^(?!\.)(?!.*\.\.)([A-Z0-9_+-.]*)[A-Z0-9_+-]@([A-Z0-9][A-Z0-9-]*\.)+[A-Z]{2,}$/i
);
Multiple environments like the Browser or Node provide a built-in URL class that can be used to validate URLs. Here we demonstrate how to leverage it to validate if a string is a valid URL.
import * as S from "@effect/schema/Schema";
const UrlString: S.Schema<string> = S.string.pipe(
S.filter((value) => {
try {
new URL(value);
return true;
} catch (_) {
return false;
}
})
);
const parse = S.decodeUnknownSync(UrlString);
console.log(parse("https://www.effect.website")); // https://www.effect.website
In case you prefer to normalize URLs you can combine transformOrFail with URL:
import * as ParseResult from "@effect/schema/ParseResult";
import * as S from "@effect/schema/Schema";
const NormalizedUrlString: S.Schema<string> = S.string.pipe(
S.filter((value) => {
try {
return new URL(value).toString() === value;
} catch (_) {
return false;
}
})
);
const NormalizeUrlString: S.Schema<string> = S.transformOrFail(
S.string,
NormalizedUrlString,
(value, _, ast) =>
ParseResult.try({
try: () => new URL(value).toString(),
catch: (err) =>
ParseResult.type(
ast,
value,
err instanceof Error ? err.message : undefined
),
}),
ParseResult.succeed
);
const parse = S.decodeUnknownSync(NormalizeUrlString);
console.log(parse("https://www.effect.website")); // "https://www.effect.website/"
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<A, I, R> {
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, I, R> as input and returns a Schema<readonly [A, A], readonly [I, I], R> as output.
First of all we need to define the signature of pair
import type * as S from "@effect/schema/Schema";
declare const pair: <A, I, R>(
schema: S.Schema<A, I, R>
) => S.Schema<readonly [A, A], readonly [I, I], R>;
Then we can implement the body using the APIs exported by the @effect/schema/AST module:
import * as AST from "@effect/schema/AST";
import * as S from "@effect/schema/Schema";
import * as Option from "effect/Option";
const pair = <A, I, R>(
schema: S.Schema<A, I, R>
): S.Schema<readonly [A, A], readonly [I, I], R> => {
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.
import * as S from "@effect/schema/Schema";
const pair = <A, I, R>(
schema: S.Schema<A, I, R>
): S.Schema<readonly [A, A], readonly [I, I], R> => S.tuple(schema, schema);
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 = <A, I, R>(self: S.Schema<A, I, R>): S.Schema<A, I, R> =>
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 AST from "@effect/schema/AST";
import * as S from "@effect/schema/Schema";
import * as Option from "effect/Option";
const DeprecatedId = Symbol.for(
"some/unique/identifier/for/the/custom/annotation"
);
const deprecated = <A, I, R>(self: S.Schema<A, I, R>): S.Schema<A, I, R> =>
S.make(AST.setAnnotation(self.ast, DeprecatedId, true));
const schema = deprecated(S.string);
const isDeprecated = <A, I, R>(schema: S.Schema<A, I, R>): boolean =>
AST.getAnnotation<boolean>(DeprecatedId)(schema.ast).pipe(
Option.getOrElse(() => false)
);
console.log(isDeprecated(S.string)); // false
console.log(isDeprecated(schema)); // true
When a parsing, decoding, or encoding process encounters a failure, a default error message is automatically generated for you. Let's explore some examples:
import * as S from "@effect/schema/Schema";
const schema = S.struct({
name: S.string,
age: S.number,
});
S.decodeUnknownSync(schema)(null);
/*
throws:
Error: Expected { name: string; age: number }, actual null
*/
S.decodeUnknownSync(schema)({}, { errors: "all" });
/*
throws:
Error: { name: string; age: number }
├─ ["name"]
│ └─ is missing
└─ ["age"]
└─ is missing
*/
When you include an identifier annotation, it will be incorporated into the default error message, followed by a description if provided:
import * as S from "@effect/schema/Schema";
const schema = S.struct({
name: S.string.pipe(S.identifier("Name")),
age: S.number.pipe(S.identifier("Age")),
}).pipe(S.identifier("Person"));
S.decodeUnknownSync(schema)(null);
/*
throws:
Error: Expected Person, actual null
*/
S.decodeUnknownSync(schema)({}, { errors: "all" });
/*
throws:
Error: Person
├─ ["name"]
│ └─ is missing
└─ ["age"]
└─ is missing
*/
S.decodeUnknownSync(schema)({ name: null, age: null }, { errors: "all" });
/*
throws:
Error: Person
├─ ["name"]
│ └─ Expected Name (a string), actual null
└─ ["age"]
└─ Expected Age (a number), actual null
*/
When a refinement fails, the default error message indicates whether the failure occurred in the "from" part or within the predicate defining the refinement:
import * as S from "@effect/schema/Schema";
const schema = S.struct({
name: S.NonEmpty.pipe(S.identifier("Name")), // refinement
age: S.Positive.pipe(S.int(), S.identifier("Age")), // refinement
}).pipe(S.identifier("Person"));
// "from" failure
S.decodeUnknownSync(schema)({ name: null, age: 18 });
/*
throws:
Error: Person
└─ ["name"]
└─ Name
└─ From side refinement failure
└─ Expected a string, actual null
*/
// predicate failure
S.decodeUnknownSync(schema)({ name: "", age: 18 });
/*
throws:
Error: Person
└─ ["name"]
└─ Name
└─ Predicate refinement failure
└─ Expected Name (a non empty string), actual ""
*/
In the first example, the error message indicates a "from" side refinement failure in the "Name" property, specifying that a string was expected but received null. In the second example, a predicate refinement failure is reported, indicating that a non-empty string was expected for "Name," but an empty string was provided.
You have the option to customize error messages for refinements using the S.message annotation:
import * as S from "@effect/schema/Schema";
const schema = S.struct({
name: S.NonEmpty.pipe(
S.identifier("Name"),
S.message(() => "Name: a required non empty string")
),
age: S.Positive.pipe(
S.int(),
S.identifier("Age"),
S.message(() => "Age: a positive integer")
),
}).pipe(S.identifier("Person"));
S.decodeUnknownSync(schema)({ name: null, age: 18 });
/*
throws:
Error: Person
└─ ["name"]
└─ Name: a required non empty string
*/
S.decodeUnknownSync(schema)({ name: "", age: 18 });
/*
throws:
Error: Person
└─ ["name"]
└─ Name: a required non empty string
*/
When setting multiple override messages, the one corresponding to the first failed predicate is used, starting from the innermost refinement to the outermost:
import * as S from "@effect/schema/Schema";
const schema = S.struct({
name: S.NonEmpty,
age: S.number.pipe(
S.message(() => "please enter a number"),
S.positive({ message: () => "please enter a positive number" }),
S.int({ message: () => "please enter an integer" })
),
}).pipe(S.identifier("Person"));
S.decodeUnknownSync(schema)({ name: "John", age: null });
/*
throws:
Error: Person
└─ ["age"]
└─ please enter a number
*/
S.decodeUnknownSync(schema)({ name: "John", age: -1 });
/*
throws:
Error: Person
└─ ["age"]
└─ please enter a positive number
*/
S.decodeUnknownSync(schema)({ name: "John", age: 1.2 });
/*
throws:
Error: Person
└─ ["age"]
└─ please enter an integer
*/
S.decodeUnknownSync(schema)({ name: "John", age: -1.2 });
/*
throws:
Error: Person
└─ ["age"]
└─ please enter a positive number
*/
When a transformation encounters an error, the default error message provides information on whether the failure happened in the "from" part, the "to" part, or within the transformation process itself:
import * as ParseResult from "@effect/schema/ParseResult";
import * as S from "@effect/schema/Schema";
const IntFromString = S.transformOrFail(
S.string,
S.Int,
(s, _, ast) => {
const n = Number(s);
return Number.isNaN(n)
? ParseResult.fail(ParseResult.type(ast, s))
: ParseResult.succeed(n);
},
(n) => ParseResult.succeed(String(n))
).pipe(S.identifier("IntFromString"));
const schema = S.struct({
name: S.NonEmpty,
age: IntFromString,
}).pipe(S.identifier("Person"));
// "from" failure
S.decodeUnknownSync(schema)({ name: "John", age: null });
/*
throws:
Error: Person
└─ ["age"]
└─ IntFromString
└─ From side transformation failure
└─ Expected a string, actual null
*/
// "to" failure
S.decodeUnknownSync(schema)({ name: "John", age: "1.2" });
/*
throws:
Error: Person
└─ ["age"]
└─ IntFromString
└─ To side transformation failure
└─ Int
└─ Predicate refinement failure
└─ Expected Int (an integer), actual 1.2
*/
// "transformation" failure
S.decodeUnknownSync(schema)({ name: "John", age: "a" });
/*
throws:
Error: Person
└─ ["age"]
└─ IntFromString
└─ Transformation process failure
└─ Expected IntFromString, actual "a"
*/
You have the option to customize error messages for transformations using the S.message annotation:
import * as ParseResult from "@effect/schema/ParseResult";
import * as S from "@effect/schema/Schema";
const IntFromString = S.transformOrFail(
S.string.pipe(S.message(() => "please enter a string")),
S.Int.pipe(S.message(() => "please enter an integer")),
(s, _, ast) => {
const n = Number(s);
return Number.isNaN(n)
? ParseResult.fail(ParseResult.type(ast, s))
: ParseResult.succeed(n);
},
(n) => ParseResult.succeed(String(n))
).pipe(
S.identifier("IntFromString"),
S.message(() => "please enter a parseable string")
);
const schema = S.struct({
name: S.NonEmpty,
age: IntFromString,
}).pipe(S.identifier("Person"));
// "from" failure
S.decodeUnknownSync(schema)({ name: "John", age: null });
/*
throws:
Error: Person
└─ ["age"]
└─ please enter a string
*/
// "to" failure
S.decodeUnknownSync(schema)({ name: "John", age: "1.2" });
/*
throws:
Error: Person
└─ ["age"]
└─ please enter an integer
*/
// "transformation" failure
S.decodeUnknownSync(schema)({ name: "John", age: "a" });
/*
throws:
Error: Person
└─ ["age"]
└─ please enter a parseable string
*/
The MIT License (MIT)
Thank you for considering contributing to our project! Here are some guidelines to help you get started:
If you have found a bug, please open an issue on our issue tracker and provide as much detail as possible. This should include:
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:
We welcome contributions via pull requests! Here are some guidelines to help you get started:
git checkout -b my-new-featurepnpm install (assuming pnpm version 8.x).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.pnpm changeset.git commit -am 'Add some feature'.git push origin my-new-feature.main branch.By contributing to this project, you agree that your contributions will be licensed under the project's MIT License.
FAQs
Modeling the schema of data structures as first-class values
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