@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<Type, Encoded, Context> 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 Encoded to an output type Type. |
| Encoding | Converting data from an output type Type back to an input type Encoded. |
| Asserting | Verifying that a value adheres to the schema's output type Type. |
| 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!
The Schema<Type, Encoded, Context> type represents an imMutable value that describes the structure of your data.
The Schema type has three type parameters with the following meanings:
Type if not explicitly provided.Effect type, it represents the contextual data required by the schema to execute both decoding and encoding. If this type parameter is never (default if not explicitly provided), it means the schema has no requirements.Examples
Schema<string> (defaulted to Schema<string, string, never>) represents a schema that decodes to string, encodes to string, and has no requirements.Schema<number, string> (defaulted to Schema<number, string, never>) represents a schema that decodes to number from string, encodes a number to a string, and has no requirements.[!NOTE] In the Effect ecosystem, you may often encounter the type parameters of
Schemaabbreviated asA,I, andRrespectively. This is just shorthand for the type value of type A, Input, and Requirements.
Schema values are imMutable, and all @effect/schema functions produce new Schema values.
Schema values do not actually do anything, they are just values that model or describe the structure of your data.
Schema values don't perform any actions themselves; they simply describe the structure of your data. A Schema can be interpreted by various "compilers" into specific operations, depending on the compiler type (decoding, encoding, pretty printing, arbitraries, etc.).
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 Person = S.Struct({
name: S.optional(S.String.pipe(S.nonEmpty()), {
exact: true
})
})
/*
type Type = {
readonly name?: string; // the type is strict (no `| undefined`)
}
*/
type Type = S.Schema.Type<typeof Person>
S.decodeSync(Person)({ name: undefined })
/*
TypeScript Error:
Argument of type '{ name: undefined; }' is not assignable to parameter of type '{ readonly name?: string; }' with 'exactOptionalPropertyTypes: true'. Consider adding 'undefined' to the types of the target's properties.
Types of property 'name' are incompatible.
Type 'undefined' is not assignable to type 'string'.ts(2379)
*/
Here, notice that the type of name is "exact" (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 Person = S.Struct({
name: S.optional(S.String.pipe(S.nonEmpty()), {
exact: true
})
})
/*
type Type = {
readonly name?: string | undefined; // the type is widened to string | undefined
}
*/
type Type = S.Schema.Type<typeof Person>
S.decodeSync(Person)({ name: undefined }) // No type error, but a decoding failure occurs
/*
Error: { name?: a non empty string }
└─ ["name"]
└─ a non empty string
└─ From side refinement failure
└─ Expected a string, actual undefined
*/
In this case, the type of name 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.
Example (Namespace Import)
import * as Schema from "@effect/schema/Schema"
Example (Named Import)
import { Schema } from "@effect/schema"
One common way to define a Schema is by utilizing the struct constructor provided by @effect/schema. This function allows you to create a new Schema that outlines an object with specific properties. Each property in the object is defined by its own Schema, which specifies the data type and any validation rules.
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
})
[!NOTE] It's important to note that by default, most constructors exported by
@effect/schemareturnreadonlytypes. For instance, in thePersonschema above, the resulting type would be{ readonly name: string; readonly age: number; }.
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.Type utility.
For instance 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.NumberFromString
})
interface Person extends S.Schema.Type<typeof Person> {}
/*
Equivalent to:
interface Person {
readonly name: string;
readonly age: number;
}
*/
Alternatively, you can define the Person type using the type keyword:
type Person = S.Schema.Type<typeof Person>
/*
Equivalent to:
type Person {
readonly name: string;
readonly age: number;
}
*/
Both approaches yield the same result, but using an interface provides benefits such as performance advantages and improved readability.
In cases where in a Schema<A, I> the I type differs from the A type, you can also extract the inferred I type using the Schema.Encoded utility.
import * as S from "@effect/schema/Schema"
const Person = S.Struct({
name: S.String,
age: S.NumberFromString
})
type PersonEncoded = S.Schema.Encoded<typeof Person>
/*
type PersonEncoded = {
readonly name: string;
readonly age: string;
}
*/
You can also extract the inferred type R that represents the context described by the schema using the Schema.Context utility:
import * as S from "@effect/schema/Schema"
const Person = S.Struct({
name: S.String,
age: S.NumberFromString
})
// type PersonContext = never
type PersonContext = S.Schema.Context<typeof Person>
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.Type<typeof _Person> {}
// Re-declare the schema to create a schema with an opaque type
const Person: S.Schema<Person> = _Person
Alternatively, you can use the Class APIs (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.Struct({
name: S.String,
age: S.NumberFromString
})
interface Person extends S.Schema.Type<typeof _Person> {}
interface PersonEncoded extends S.Schema.Encoded<typeof _Person> {}
// Re-declare the schema to create a schema with an opaque type
const Person: S.Schema<Person, PersonEncoded> = _Person
In this case, the field "age" is of type string in the Encoded type of the schema and is of type number in the Type type of the schema. Therefore, we need to define two interfaces (PersonEncoded and Person) and use both to redeclare our final schema Person.
When working with unknown data types in TypeScript, decoding them into a known structure can be challenging. Luckily, @effect/schema provides several functions to help with this process. Let's explore how to decode unknown values using these functions.
decodeUnknown* FunctionsThe @effect/schema/Schema module offers a variety of decodeUnknown* functions, each tailored for different decoding scenarios:
decodeUnknownSync: Synchronously decodes a value and throws an error if parsing fails.decodeUnknownOption: Decodes a value and returns an Option type.decodeUnknownEither: Decodes a value and returns an Either type.decodeUnknownPromise: Decodes a value and returns a Promise.decodeUnknown: Decodes a value and returns an Effect.Example (Using decodeUnknownSync)
Let's begin with an example using the decodeUnknownSync function. This function is useful when you want to parse a value and immediately throw an error if the parsing fails.
import * as S from "@effect/schema/Schema"
const Person = S.Struct({
name: S.String,
age: S.Number
})
// Simulate an unknown input
const input: unknown = { name: "Alice", age: 30 }
console.log(S.decodeUnknownSync(Person)(input))
// Output: { name: 'Alice', age: 30 }
console.log(S.decodeUnknownSync(Person)(null))
/*
throws:
Error: Expected { name: string; age: number }, actual null
*/
Example (Using decodeUnknownEither)
Now, let's see how to use the decodeUnknownEither function, which returns an Either type representing success or failure.
import * as S from "@effect/schema/Schema"
import * as Either from "effect/Either"
const Person = S.Struct({
name: S.String,
age: S.Number
})
const decode = S.decodeUnknownEither(Person)
// Simulate an unknown input
const input: unknown = { name: "Alice", age: 30 }
const result1 = decode(input)
if (Either.isRight(result1)) {
console.log(result1.right)
/*
Output:
{ name: "Alice", age: 30 }
*/
}
const result2 = decode(null)
if (Either.isLeft(result2)) {
console.log(result2.left)
/*
Output:
{
_id: 'ParseError',
message: 'Expected { name: string; age: number }, actual null'
}
*/
}
The decode function returns an Either<A, ParseError>, 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<A, ParseError> 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.
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
decode: (id) =>
Effect.succeed({ id, name: "name", age: 18 }).pipe(
Effect.delay("10 millis")
),
encode: (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": "(number <-> { id: number; name: string; age: number })\n└─ cannot be be resolved synchronously, this is caused by using runSync on an effect that performs async work"
}
}
*/
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 }
*/
[!NOTE] The
onExcessPropertyanderroroptions also affect encoding.
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"
*/
[!NOTE] The
onExcessPropertyanderroroptions also affect encoding.
The @effect/schema/Schema module provides several encode* functions to encode data according to a schema:
encodeSync: Synchronously encodes data and throws an error if encoding fails.encodeOption: Encodes data and returns an Option type.encodeEither: Encodes data and returns an Either type representing success or failure.encodePromise: Encodes data and returns a Promise.encode: Encodes data and returns an Effect.Let's consider an example where we have a schema for a Person object with a name property of type string and an age property of type number.
import * as S from "@effect/schema/Schema"
// 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.NonEmpty,
age: Age
})
console.log(S.encodeSync(Person)({ name: "Alice", age: 30 }))
// Output: { name: 'Alice', age: '30' }
console.log(S.encodeSync(Person)({ name: "", age: 30 }))
/*
throws:
Error: { name: NonEmpty; age: NumberFromString }
└─ ["name"]
└─ NonEmpty
└─ Predicate refinement failure
└─ Expected NonEmpty (a non empty string), actual ""
*/
Note that during encoding, the number value 30 was converted to a string "30".
[!NOTE] The
onExcessPropertyanderroroptions also affect encoding.
When you're working with Effect Schema and encounter errors during decoding, or encoding functions, you can format these errors in two different ways: using the TreeFormatter or the ArrayFormatter.
The TreeFormatter is the default method for formatting errors. It organizes errors in a tree structure, providing a clear 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("Decoding failed:")
console.error(formatError(result.left))
}
/*
Decoding failed:
{ name: string; age: number }
└─ ["name"]
└─ is missing
*/
In this example, the tree error message is structured as follows:
{ name: string; age: number } represents the schema, providing a visual representation of the expected structure. This can be customized using annotations, such as setting the identifier annotation.["name"] indicates the offending property, in this case, the "name" property.is missing represents the specific error for the "name" property.When a decoding or encoding operation fails, it's useful to have additional details in the default error message returned by TreeFormatter to understand exactly which value caused the operation to fail. To achieve this, you can set an annotation that depends on the value undergoing the operation and can return an excerpt of it, making it easier to identify the problematic value. A common scenario is when the entity being validated has an id field. The ParseIssueTitle annotation facilitates this kind of analysis during error handling.
The type of the annotation is:
export type ParseIssueTitleAnnotation = (
issue: ParseIssue
) => string | undefined
If you set this annotation on a schema and the provided function returns a string, then that string is used as the title by TreeFormatter, unless a message annotation (which has the highest priority) has also been set. If the function returns undefined, then the default title used by TreeFormatter is determined with the following priorities:
identifiertitledescriptionast.toString()Example
import type { ParseIssue } from "@effect/schema/ParseResult"
import * as S from "@effect/schema/Schema"
const getOrderItemId = ({ actual }: ParseIssue) => {
if (S.is(S.Struct({ id: S.String }))(actual)) {
return `OrderItem with id: ${actual.id}`
}
}
const OrderItem = S.Struct({
id: S.String,
name: S.String,
price: S.Number
}).annotations({
identifier: "OrderItem",
parseIssueTitle: getOrderItemId
})
const getOrderId = ({ actual }: ParseIssue) => {
if (S.is(S.Struct({ id: S.Number }))(actual)) {
return `Order with id: ${actual.id}`
}
}
const Order = S.Struct({
id: S.Number,
name: S.String,
items: S.Array(OrderItem)
}).annotations({
identifier: "Order",
parseIssueTitle: getOrderId
})
const decode = S.decodeUnknownSync(Order, { errors: "all" })
// No id available, so the `identifier` annotation is used as the title
decode({})
/*
throws
Error: Order
├─ ["id"]
│ └─ is missing
├─ ["name"]
│ └─ is missing
└─ ["items"]
└─ is missing
*/
// An id is available, so the `parseIssueTitle` annotation is used as the title
decode({ id: 1 })
/*
throws
Error: Order with id: 1
├─ ["name"]
│ └─ is missing
└─ ["items"]
└─ is missing
*/
decode({ id: 1, items: [{ id: "22b", price: "100" }] })
/*
throws
Error: Order with id: 1
├─ ["name"]
│ └─ is missing
└─ ["items"]
└─ ReadonlyArray<OrderItem>
└─ [0]
└─ OrderItem with id: 22b
├─ ["name"]
│ └─ is missing
└─ ["price"]
└─ Expected a number, actual "100"
*/
In the examples above, we can see how the parseIssueTitle annotation helps provide meaningful error messages when decoding fails.
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.
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 make 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 { Arbitrary, FastCheck, Schema } from "@effect/schema"
const Person = Schema.Struct({
name: Schema.String,
age: Schema.String.pipe(Schema.compose(Schema.NumberFromString), Schema.int())
})
/*
FastCheck.Arbitrary<{
readonly name: string;
readonly age: number;
}>
*/
const PersonArbitraryType = Arbitrary.make(Person)
console.log(FastCheck.sample(PersonArbitraryType, 2))
/*
Output:
[ { name: 'iP=!', age: -6 }, { name: '', age: 14 } ]
*/
/*
Arbitrary for the "Encoded" type:
FastCheck.Arbitrary<{
readonly name: string;
readonly age: string;
}>
*/
const PersonArbitraryEncoded = Arbitrary.make(Schema.encodedSchema(Person))
console.log(FastCheck.sample(PersonArbitraryEncoded, 2))
/*
Output:
[ { name: '{F', age: '$"{|' }, { name: 'nB}@BK', age: '^V+|W!Z' } ]
*/
You can customize the output by using the arbitrary annotation:
import { Arbitrary, FastCheck, Schema } from "@effect/schema"
const schema = Schema.Number.annotations({
arbitrary: () => (fc) => fc.nat()
})
const arb = Arbitrary.make(schema)
console.log(FastCheck.sample(arb, 2))
// Output: [ 1139348969, 749305462 ]
[!WARNING] Note that when customizing any schema, any filter preceding the customization will be lost, only filters following the customization will be respected.
Example
import { Arbitrary, FastCheck, Schema } from "@effect/schema"
const bad = Schema.Number.pipe(Schema.positive()).annotations({
arbitrary: () => (fc) => fc.integer()
})
console.log(FastCheck.sample(Arbitrary.make(bad), 2))
// Example Output: [ -1600163302, -6 ]
const good = Schema.Number.annotations({
arbitrary: () => (fc) => fc.integer()
}).pipe(Schema.positive())
console.log(FastCheck.sample(Arbitrary.make(good), 2))
// Example Output: [ 7, 1518247613 ]
The make 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 make 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 }'
*/
You can customize the output using the pretty annotation:
import * as Pretty from "@effect/schema/Pretty"
import * as S from "@effect/schema/Schema"
const schema = S.Number.annotations({
pretty: () => (n) => `my format: ${n}`
})
console.log(Pretty.make(schema)(1)) // my format: 1
The make function from the @effect/schema/JSONSchema module enables you to create a JSON Schema based on a defined schema:
import * as JSONSchema from "@effect/schema/JSONSchema"
import * as S from "@effect/schema/Schema"
const Person = S.Struct({
name: S.String,
age: S.Number
})
const jsonSchema = JSONSchema.make(Person)
console.log(JSON.stringify(jsonSchema, null, 2))
/*
Output:
{
"$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.make 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.annotations({ identifier: "Name" })
const Age = S.Number.annotations({ 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))
}).annotations({ 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 make 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
})
// $ExpectType Equivalence<{ readonly name: string; readonly age: number; }>
const PersonEquivalence = Equivalence.make(Person)
const john = { name: "John", age: 23 }
const alice = { name: "Alice", age: 30 }
console.log(PersonEquivalence(john, { name: "John", age: 23 })) // Output: true
console.log(PersonEquivalence(john, alice)) // Output: false
You can customize the output using the equivalence annotation:
import * as Equivalence from "@effect/schema/Equivalence"
import * as S from "@effect/schema/Schema"
const schema = S.String.annotations({
equivalence: () => (a, b) => a.at(0) === b.at(0)
})
console.log(Equivalence.make(schema)("aaa", "abb")) // Output: true
An "API Interface" is an interface specifically defined for a schema exported from @effect/schema or for a particular API exported from @effect/schema. Let's see an example with a simple schema:
Example (an Age schema)
import * as S from "@effect/schema/Schema"
// API interface
interface Age extends S.Schema<number> {}
const Age: Age = S.Number.pipe(S.between(0, 100))
// type AgeType = number
type AgeType = S.Schema.Type<typeof Age>
// type AgeEncoded = number
type AgeEncoded = S.Schema.Encoded<typeof Age>
The benefit is that when we hover over the Age schema, we see Age instead of Schema<number, number, never>. This is a small improvement if we only think about the Age schema, but as we'll see shortly, these improvements in schema visualization add up, resulting in a significant improvement in the readability of our schemas.
Many of the built-in schemas exported from @effect/schema have been equipped with API interfaces, for example number or never.
import * as S from "@effect/schema/Schema"
// const number: S.$Number
S.Number
// const never: S.Never
S.Never
[!NOTE] Notice that we had to add a
$suffix to the API interface name because we couldn't simply use "Number" since it's a reserved name for the TypeScriptNumbertype.
Now let's see an example with a combinator that, given an input schema for a certain type A, returns the schema of the pair readonly [A, A]:
Example (a pair combinator)
import * as S from "@effect/schema/Schema"
// API interface
export interface pair<S extends S.Schema.Any>
extends S.Schema<
readonly [S.Schema.Type<S>, S.Schema.Type<S>],
readonly [S.Schema.Encoded<S>, S.Schema.Encoded<S>],
S.Schema.Context<S>
> {}
// API
export const pair = <S extends S.Schema.Any>(schema: S): pair<S> =>
S.Tuple(S.asSchema(schema), S.asSchema(schema))
[!NOTE] The
S.Schema.Anyhelper represents any schema, except fornever. For more information on theasSchemahelper, refer to the following section "Understanding Opaque Names".
If we try to use our pair combinator, we see that readability is also improved in this case:
// const Coords: pair<S.$Number>
const Coords = pair(S.Number)
In hover, we simply see pair<S.$Number> instead of the verbose:
// const Coords: S.Schema<readonly [number, number], readonly [number, number], never>
const Coords = S.Tuple(S.Number, S.Number)
The new name is not only shorter and more readable but also carries along the origin of the schema, which is a call to the pair combinator.
Opaque names generated in this way are very convenient, but sometimes there's a need to see what the underlying types are, perhaps for debugging purposes while you declare your schemas. At any time, you can use the asSchema function, which returns an Schema<A, I, R> compatible with your opaque definition:
// const Coords: pair<S.$Number>
const Coords = pair(S.Number)
// const NonOpaqueCoords: S.Schema<readonly [number, number], readonly [number, number], never>
const NonOpaqueCoords = S.asSchema(Coords)
[!NOTE] The call to
asSchemais negligible in terms of overhead since it's nothing more than a glorified identity function.
Many of the built-in combinators exported from @effect/schema have been equipped with API interfaces, for example struct:
import * as S from "@effect/schema/Schema"
/*
const Person: S.Struct<{
name: S.$String;
age: S.$Number;
}>
*/
const Person = S.Struct({
name: S.String,
age: S.Number
})
In hover, we simply see:
const Person: S.Struct<{
name: S.$String
age: S.$Number
}>
instead of the verbose:
const Person: S.Schema<
{
readonly name: string
readonly age: number
},
{
readonly name: string
readonly age: number
},
never
>
The benefits of API interfaces don't end with better readability; in fact, the driving force behind the introduction of API interfaces arises more from the need to expose some important information about the schemas that users generate. Let's see some examples related to literals and structs:
Example (exposed literals)
Now when we define literals, we can retrieve them using the literals field exposed by the generated schema:
import * as S from "@effect/schema/Schema"
// const myliterals: S.Literal<["A", "B"]>
const myliterals = S.Literal("A", "B")
// literals: readonly ["A", "B"]
myliterals.literals
console.log(myliterals.literals) // Output: [ 'A', 'B' ]
Example (exposed fields)
Similarly to what we've seen for literals, when we define a struct, we can retrieve its fields:
import * as S from "@effect/schema/Schema"
/*
const Person: S.Struct<{
name: S.$String;
age: S.$Number;
}>
*/
const Person = S.Struct({
name: S.String,
age: S.Number
})
/*
fields: {
readonly name: S.$String;
readonly age: S.$Number;
}
*/
Person.fields
console.log(Person.fields)
/*
{
name: Schema {
ast: StringKeyword { _tag: 'StringKeyword', annotations: [Object] },
...
},
age: Schema {
ast: NumberKeyword { _tag: 'NumberKeyword', annotations: [Object] },
...
}
}
*/
Being able to retrieve the fields is particularly advantageous when you want to extend a struct with new fields; now you can do it simply using the spread operator:
import * as S from "@effect/schema/Schema"
const Person = S.Struct({
name: S.String,
age: S.Number
})
/*
const PersonWithId: S.Struct<{
id: S.$Number;
name: S.$String;
age: S.$Number;
}>
*/
const PersonWithId = S.Struct({
...Person.fields,
id: S.Number
})
The list of APIs equipped with API interfaces is extensive; here we provide only the main ones just to give you an idea of the new development possibilities that have opened up:
import * as S from "@effect/schema/Schema"
// ------------------------
// array value
// ------------------------
// value: S.$String
S.Array(S.String).value
// ------------------------
// record key and value
// ------------------------
// key: S.$String
S.Record(S.String, S.Number).key
// value: S.$Number
S.Record(S.String, S.Number).value
// ------------------------
// union members
// ------------------------
// members: readonly [S.$String, S.$Number]
S.Union(S.String, S.Number).members
// ------------------------
// tuple elements
// ------------------------
// elements: readonly [S.$String, S.$Number]
S.Tuple(S.String, S.Number).elements
All the API interfaces equipped with schemas and built-in combinators are compatible with the annotations method, meaning that their type is not lost but remains the original one before annotation:
import * as S from "@effect/schema/Schema"
// const Name: S.$String
const Name = S.String.annotations({ identifier: "Name" })
As you can see, the type of Name is still $String and has not been lost, becoming Schema<string, string, never>.
This doesn't happen by default with API interfaces defined in userland:
import * as S from "@effect/schema/Schema"
// API interface
interface Age extends S.Schema<number> {}
const Age: Age = S.Number.pipe(S.between(0, 100))
// const AnotherAge: S.Schema<number, number, never>
const AnotherAge = Age.annotations({ identifier: "AnotherAge" })
However, the fix is very simple; just modify the definition of the Age API interface using the Annotable interface exported by @effect/schema:
import * as S from "@effect/schema/Schema"
// API interface
interface Age extends S.Annotable<Age, number> {}
const Age: Age = S.Number.pipe(S.between(0, 100))
// const AnotherAge: Age
const AnotherAge = Age.annotations({ identifier: "AnotherAge" })
| Typescript Type | Description / Notes | Schema / Combinator |
|---|---|---|
null | S.Null | |
undefined | S.Undefined | |
string | S.String | |
number | S.Number | |
boolean | S.Boolean | |
symbol | S.SymbolFromSelf / S.Symbol | |
BigInt | S.BigIntFromSelf / S.BigInt | |
unknown | S.Unknown | |
any | S.Any | |
never | S.Never | |
object | S.Object | |
unique symbol | S.UniqueSymbolFromSelf | |
"a", 1, true | type literals | S.Literal("a"), S.Literal(1), S.Literal(true) |
a${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 | undefined } | optional fields | S.Struct({ a: S.optional(S.String) }) |
{ 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) |
Here are the primitive schemas provided by the @effect/schema/Schema module:
import * as S from "@effect/schema/Schema"
S.String // Schema<string>
S.Number // Schema<number>
S.Boolean // Schema<boolean>
S.BigIntFromSelf // Schema<BigInt>
S.SymbolFromSelf // Schema<symbol>
S.Object // Schema<object>
S.Undefined // Schema<undefined>
S.Void // Schema<void>
S.Any // Schema<any>
S.Unknown // Schema<unknown>
S.Never // Schema<never>
These primitive schemas are building blocks for creating more complex schemas to describe your data structures.
Literals in schemas represent specific values that are directly specified. Here are some examples of literal schemas provided by the @effect/schema/Schema module:
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)
We can also use pickLiteral with a literal schema to narrow down the possible values:
import * as S from "@effect/schema/Schema"
S.Literal("a", "b", "c").pipe(S.pickLiteral("a", "b")) //same as S.Literal("a", "b")
Sometimes, we need to reuse a schema literal in other parts of our code. Let's see an example:
import * as S from "@effect/schema/Schema"
const FruitId = S.Number
// the source of truth regarding the Fruit category
const FruitCategory = S.Literal("sweet", "citrus", "tropical")
const Fruit = S.Struct({
id: FruitId,
category: FruitCategory
})
// Here, we want to reuse our FruitCategory definition to create a subtype of Fruit
const SweetAndCitrusFruit = S.Struct({
fruitId: FruitId,
category: FruitCategory.pipe(S.pickLiteral("sweet", "citrus"))
/*
By using pickLiteral from the FruitCategory, we ensure that the values selected
are those defined in the category definition above.
If we remove "sweet" from the FruitCategory definition, TypeScript will notify us.
*/
})
In this example, FruitCategory serves as the source of truth for the categories of fruits. We reuse it to create a subtype of Fruit called SweetAndCitrusFruit, ensuring that only the categories defined in FruitCategory are allowed.
You can access the literals of a literal schema:
import * as S from "@effect/schema/Schema"
const schema = S.Literal("a", "b")
// Accesses the literals
const literals = schema.literals // readonly ["a", "b"]
The TemplateLiteral constructor 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"))
import * as S from "@effect/schema/Schema"
const mySymbol = Symbol.for("mysymbol")
// const mySymbolSchema: S.Schema<typeof mySymbol>
const mySymbolSchema = S.UniqueSymbolFromSelf(mySymbol)
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
*/
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(issue) 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(
new 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. If you intend to modify theType, consider using Branded types.
import * as S from "@effect/schema/Schema"
S.String.pipe(S.maxLength(5)) // Specifies maximum length of a string
S.String.pipe(S.minLength(5)) // Specifies minimum length of a string
S.NonEmpty // Equivalent to ensuring the string has a minimum length of 1
S.String.pipe(S.length(5)) // Specifies exact length of a string
S.String.pipe(S.length({ min: 2, max: 4 })) // Specifies a range for the length of a string
S.String.pipe(S.pattern(regex)) // Matches a string against a regular expression pattern
S.String.pipe(S.startsWith(string)) // Ensures a string starts with a specific substring
S.String.pipe(S.endsWith(string)) // Ensures a string ends with a specific substring
S.String.pipe(S.includes(searchString)) // Checks if a string includes a specific substring
S.String.pipe(S.trimmed()) // Validates that a string has no leading or trailing whitespaces
S.String.pipe(S.lowercased()) // Validates that a string is entirely in lowercase
[!NOTE] The
trimmedcombinator does not make any transformations, it only validates. If what you were looking for was a combinator to trim strings, then check out thetrimcombinator ot theTrimschema.
import * as S from "@effect/schema/Schema"
S.Number.pipe(S.greaterThan(5)) // Specifies a number greater than 5
S.Number.pipe(S.greaterThanOrEqualTo(5)) // Specifies a number greater than or equal to 5
S.Number.pipe(S.lessThan(5)) // Specifies a number less than 5
S.Number.pipe(S.lessThanOrEqualTo(5)) // Specifies a number less than or equal to 5
S.Number.pipe(S.between(-2, 2)) // Specifies a number between -2 and 2, inclusive
S.Number.pipe(S.int()) // Specifies that the value must be an integer
S.Number.pipe(S.nonNaN()) // Ensures the value is not NaN
S.Number.pipe(S.finite()) // Ensures the value is finite and not Infinity or -Infinity
S.Number.pipe(S.positive()) // Specifies a positive number (> 0)
S.Number.pipe(S.nonNegative()) // Specifies a non-negative number (>= 0)
S.Number.pipe(S.negative()) // Specifies a negative number (< 0)
S.Number.pipe(S.nonPositive()) // Specifies a non-positive number (<= 0)
S.Number.pipe(S.multipleOf(5)) // Specifies a number that is evenly divisible by 5
import * as S from "@effect/schema/Schema"
S.BigInt.pipe(S.greaterThanBigInt(5n)) // Specifies a BigInt greater than 5
S.BigInt.pipe(S.greaterThanOrEqualToBigInt(5n)) // Specifies a BigInt greater than or equal to 5
S.BigInt.pipe(S.lessThanBigInt(5n)) // Specifies a BigInt less than 5
S.BigInt.pipe(S.lessThanOrEqualToBigInt(5n)) // Specifies a BigInt less than or equal to 5
S.BigInt.pipe(S.betweenBigInt(-2n, 2n)) // Specifies a BigInt between -2 and 2, inclusive
S.BigInt.pipe(S.positiveBigInt()) // Specifies a positive BigInt (> 0n)
S.BigInt.pipe(S.nonNegativeBigInt()) // Specifies a non-negative BigInt (>= 0n)
S.BigInt.pipe(S.negativeBigInt()) // Specifies a negative BigInt (< 0n)
S.BigInt.pipe(S.nonPositiveBigInt()) // Specifies a non-positive BigInt (<= 0n)
import * as S from "@effect/schema/Schema"
import * as BigDecimal from "effect/BigDecimal"
S.BigDecimal.pipe(S.greaterThanBigDecimal(BigDecimal.fromNumber(5))) // Specifies a BigDecimal greater than 5
S.BigDecimal.pipe(S.greaterThanOrEqualToBigDecimal(BigDecimal.fromNumber(5))) // Specifies a BigDecimal greater than or equal to 5
S.BigDecimal.pipe(S.lessThanBigDecimal(BigDecimal.fromNumber(5))) // Specifies a BigDecimal less than 5
S.BigDecimal.pipe(S.lessThanOrEqualToBigDecimal(BigDecimal.fromNumber(5))) // Specifies a BigDecimal less than or equal to 5
S.BigDecimal.pipe(
S.betweenBigDecimal(BigDecimal.fromNumber(-2), BigDecimal.fromNumber(2))
) // Specifies a BigDecimal between -2 and 2, inclusive
S.BigDecimal.pipe(S.positiveBigDecimal()) // Specifies a positive BigDecimal (> 0)
S.BigDecimal.pipe(S.nonNegativeBigDecimal()) // Specifies a non-negative BigDecimal (>= 0)
S.BigDecimal.pipe(S.negativeBigDecimal()) // Specifies a negative BigDecimal (< 0)
S.BigDecimal.pipe(S.nonPositiveBigDecimal()) // Specifies a non-positive BigDecimal (<= 0)
import * as S from "@effect/schema/Schema"
S.Duration.pipe(S.greaterThanDuration("5 seconds")) // Specifies a duration greater than 5 seconds
S.Duration.pipe(S.greaterThanOrEqualToDuration("5 seconds")) // Specifies a duration greater than or equal to 5 seconds
S.Duration.pipe(S.lessThanDuration("5 seconds")) // Specifies a duration less than 5 seconds
S.Duration.pipe(S.lessThanOrEqualToDuration("5 seconds")) // Specifies a duration less than or equal to 5 seconds
S.Duration.pipe(S.betweenDuration("5 seconds", "10 seconds")) // Specifies a duration between 5 seconds and 10 seconds, inclusive
import * as S from "@effect/schema/Schema"
S.Array(S.Number).pipe(S.maxItems(2)) // Specifies the maximum number of items in the array
S.Array(S.Number).pipe(S.minItems(2)) // Specifies the minimum number of items in the array
S.Array(S.Number).pipe(S.itemsCount(2)) // Specifies the exact number of items in the array
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.Type<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.Type<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
}
// S.enums<typeof Fruits>
S.Enums(Fruits)
Enums are exposed under an enums property of the schema:
// Access the enum members
S.Enums(Fruits).enums // Returns all enum members
S.Enums(Fruits).enums.Apple // Access the Apple member
S.Enums(Fruits).enums.Banana // Access the Banana member
import * as S from "@effect/schema/Schema"
// Represents a schema for a string or null value
S.NullOr(S.String)
// Represents a schema for a string, null, or undefined value
S.NullishOr(S.String)
// Represents a schema for a string or undefined value
S.UndefinedOr(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(
S.Struct({ ...Circle.fields, kind: S.Literal("circle") }), // Add a "kind" property with the literal value "circle" to Circle
{
decode: (circle) => ({ ...circle, kind: "circle" as const }), // Add the discriminant property to Circle
encode: ({ kind: _kind, ...rest }) => rest // Remove the discriminant property
}
)
),
Square.pipe(
S.transform(
S.Struct({ ...Square.fields, kind: S.Literal("square") }), // Add a "kind" property with the literal value "square" to Square
{
decode: (square) => ({ ...square, kind: "square" as const }), // Add the discriminant property to Square
encode: ({ 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
}
)
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 }
)
You can access the members of a union schema:
import * as S from "@effect/schema/Schema"
const schema = S.Union(S.String, S.Number)
// Accesses the members of the union
const members = schema.members // readonly [S.$String, S.$Number]
To define a tuple with required elements, you simply specify the list of elements:
import * as S from "@effect/schema/Schema"
// const opaque: S.Tuple<[S.$String, S.$Number]>
const opaque = S.Tuple(S.String, S.Number)
// const nonOpaque: S.Schema<readonly [string, number], readonly [string, number], never>
const nonOpaque = S.asSchema(opaque)
import * as S from "@effect/schema/Schema"
// Schema<readonly [string, number]>
const tuple1 = S.Tuple(S.String, S.Number)
// Schema<readonly [string, number, boolean]>
const tuple2 = S.Tuple(...tuple1.elements, S.Boolean)
To define an optional element, wrap the schema of the element with the OptionalElement modifier:
import * as S from "@effect/schema/Schema"
// const opaque: S.Tuple<[S.$String, S.OptionalElement<S.$Number>]>
const opaque = S.Tuple(S.String, S.optionalElement(S.Number))
// const nonOpaque: S.Schema<readonly [string, number?], readonly [string, number?], never>
const nonOpaque = S.asSchema(opaque)
To define rest elements, follow the list of elements (required or optional) with an element for the rest:
import * as S from "@effect/schema/Schema"
// const opaque: S.TupleType<readonly [S.$String, S.OptionalElement<S.$Number>], [S.$Boolean]>
const opaque = S.Tuple([S.String, S.optionalElement(S.Number)], S.Boolean)
// const nonOpaque: S.Schema<readonly [string, number?, ...boolean[]], readonly [string, number?, ...boolean[]], never>
const nonOpaque = S.asSchema(opaque)
Optionally, you can include other elements after the rest:
import * as S from "@effect/schema/Schema"
// const opaque: S.TupleType<readonly [S.$String, S.OptionalElement<S.$Number>], [S.$Boolean, S.$String]>
const opaque = S.Tuple(
[S.String, S.optionalElement(S.Number)],
S.Boolean,
S.String
)
// const nonOpaque: S.Schema<readonly [string, number | undefined, ...boolean[], string], readonly [string, number | undefined, ...boolean[], string], never>
const nonOpaque = S.asSchema(opaque)
You can access the elements and rest elements of a tuple schema:
import * as S from "@effect/schema/Schema"
const schema = S.Tuple(
[S.String, S.optionalElement(S.Number)],
S.Boolean,
S.Number
)
// Accesses the elements of the tuple
const tupleElements = schema.elements // readonly [S.$String, S.OptionalElement<S.$Number>]
// Accesses the rest elements of the tuple
const restElements = schema.rest // readonly [S.$Boolean, S.$Number]
import * as S from "@effect/schema/Schema"
// S.$Array<S.$Number>
const opaque = S.Array(S.Number)
// S.Schema<readonly number[]>
const schema = S.asSchema(opaque)
You can access the value of an array schema:
import * as S from "@effect/schema/Schema"
const schema = S.Array(S.String)
// Accesses the value
const value = schema.value // S.$String
By default, when you use S.Array, it generates a type marked as readonly. The Mutable combinator is a useful function for creating a new schema with a mutable type in a shallow manner:
import * as S from "@effect/schema/Schema"
// S.mutable<S.$Array<S.$Number>>
const opaque = S.mutable(S.Array(S.Number))
// S.Schema<number[]>
const schema = S.asSchema(opaque)
import * as S from "@effect/schema/Schema"
// S.NonEmptyArray<S.$Number>
const opaque = S.NonEmptyArray(S.Number)
// S.Schema<readonly [number, ...number[]]>
const schema = S.asSchema(opaque)
You can access the value of a non-empty array schema:
import * as S from "@effect/schema/Schema"
const schema = S.NonEmptyArray(S.String)
// Accesses the value
const value = schema.value // S.$String
import * as S from "@effect/schema/Schema"
// S.$Record<S.$String, S.$Number>
const opaque1 = S.Record(S.String, S.Number)
// S.Schema<{ readonly [x: string]: number; }>
const schema1 = S.asSchema(opaque1)
// S.$Record<S.Union<[S.Literal<["a"]>, S.Literal<["b"]>]>, S.$Number>
const opaque2 = S.Record(S.Union(S.Literal("a"), S.Literal("b")), S.Number)
// S.Schema<{ readonly a: number; readonly b: number; }>
const schema2 = S.asSchema(opaque2)
import * as S from "@effect/schema/Schema"
// S.$Record<S.Schema<string, string, never>, S.$Number>
const opaque = S.Record(S.String.pipe(S.minLength(2)), S.Number)
// S.Schema<{ readonly [x: string]: number; }>
const schema = S.asSchema(opaque)
import * as S from "@effect/schema/Schema"
// S.$Record<S.SymbolFromSelf, S.$Number>
const opaque = S.Record(S.SymbolFromSelf, S.Number)
// S.Schema<{ readonly [x: symbol]: number; }>
const schema = S.asSchema(opaque)
import * as S from "@effect/schema/Schema"
// S.$Record<S.Schema<`a${string}`, `a${string}`, never>, S.$Number>
const opaque = S.Record(S.TemplateLiteral(S.Literal("a"), S.String), S.Number)
// S.Schema<{ readonly [x: `a${string}`]: number; }>
const schema = S.asSchema(opaque)
By default, when you use S.Record, it generates a type marked as readonly. The mutable combinator is a useful function for creating a new schema with a Mutable type in a shallow manner:
import * as S from "@effect/schema/Schema"
// S.mutable<S.$Record<S.$String, S.$Number>>
const opaque = S.mutable(S.Record(S.String, S.Number))
// S.Schema<{ [x: string]: number; }>
const schema = S.asSchema(opaque)
You can access the key and the value of a record schema:
import * as S from "@effect/schema/Schema"
const schema = S.Record(S.String, S.Number)
// Accesses the key
const key = schema.key // S.$String
// Accesses the value
const value = schema.value // S.$Number
import * as S from "@effect/schema/Schema"
// const opaque: S.Struct<{ a: S.$String; b: S.$Number; }>
const opaque = S.Struct({ a: S.String, b: S.Number })
// S.Schema<{ readonly a: string; readonly b: number; }>
const schema = S.asSchema(opaque)
The struct constructor optionally accepts a list of key/value pairs representing index signatures:
const struct = (props, ...indexSignatures)
Example
import * as S from "@effect/schema/Schema"
/*
const opaque: S.TypeLiteral<{
a: S.$Number;
}, readonly [{
readonly key: S.$String;
readonly value: S.$Number;
}]>
*/
const opaque = S.Struct(
{
a: S.Number
},
{ key: S.String, value: S.Number }
)
/*
const nonOpaque: S.Schema<{
readonly [x: string]: number;
readonly a: number;
}, {
readonly [x: string]: number;
readonly a: number;
}, never>
*/
const nonOpaque = S.asSchema(opaque)
Since the record constructor returns a schema that exposes both the key and the value, instead of passing a bare object { key, value }, you can use the record constructor:
import * as S from "@effect/schema/Schema"
/*
const opaque: S.TypeLiteral<{
a: S.$Number;
}, readonly [S.Record<S.$String, S.$Number>]>
*/
const opaque = S.Struct({ a: S.Number }, S.Record(S.String, S.Number))
/*
const nonOpaque: S.Schema<{
readonly [x: string]: number;
readonly a: number;
}, {
readonly [x: string]: number;
readonly a: number;
}, never>
*/
const nonOpaque = S.asSchema(opaque)
You can access the fields and the records of a struct schema:
import * as S from "@effect/schema/Schema"
const schema = S.Struct({ a: S.Number }, S.Record(S.String, S.Number))
// Accesses the fields
const fields = schema.fields // { readonly a: S.$Number; }
// Accesses the records
const records = schema.records // [S.Record<S.$String, S.$Number>]
By default, when you use S.struct, it generates a type with properties that are marked as readonly. The Mutable combinator is a useful function for creating a new schema with properties made Mutable in a shallow manner:
import * as S from "@effect/schema/Schema"
// const opaque: S.mutable<S.Struct<{ a: S.$String; b: S.$Number; }>>
const opaque = S.mutable(S.Struct({ a: S.String, b: S.Number }))
// S.Schema<{ a: string; b: number; }>
const schema = S.asSchema(opaque)
A PropertySignature generally represents a transformation from a "From" field:
{
fromKey: fromType
}
to a "To" field:
{
toKey: toType
}
Let's start with the simple definition of a property signature that can be used to add annotations:
import * as S from "@effect/schema/Schema"
/*
const Person: S.Struct<{
name: S.$String;
age: S.PropertySignature<":", number, never, ":", string, never>;
}>
*/
const Person = S.Struct({
name: S.String,
age: S.propertySignature(S.NumberFromString).annotations({ title: "Age" })
})
Let's delve into the details of all the information contained in the type of a PropertySignature:
age: PropertySignature<ToToken, ToType, FromKey, FromToken, FromType, Context>
age: is the key of the "To" fieldToToken: either "?:" or ":", "?:" indicates that the "To" field is optional, ":" indicates that the "To" field is requiredToType: the type of the "To" fieldFromKey (optional, default = never): indicates the key from the field from which the transformation starts, by default it is equal to the key of the "To" field (i.e., "age" in this case)FormToken: either "?:" or ":", "?:" indicates that the "From" field is optional, ":" indicates that the "From" field is requiredFromType: the type of the "From" fieldIn our case, the type
PropertySignature<":", number, never, ":", string, never>
indicates that there is the following transformation:
age is the key of the "To" fieldToToken = ":" indicates that the age field is requiredToType = number indicates that the type of the age field is numberFromKey = never indicates that the decoding occurs from the same field named ageFormToken = "." indicates that the decoding occurs from a required age fieldFromType = string indicates that the decoding occurs from a string type age fieldLet's see an example of decoding:
console.log(S.decodeUnknownSync(Person)({ name: "name", age: "18" }))
// Output: { name: 'name', age: 18 }
Now, suppose the field from which decoding occurs is named "AGE", but for our model, we want to keep the name in lowercase "age". To achieve this result, we need to map the field key from "AGE" to "age", and to do that, we can use the fromKey combinator:
import * as S from "@effect/schema/Schema"
/*
const Person: S.Struct<{
name: S.$String;
age: S.PropertySignature<":", number, "AGE", ":", string, never>;
}>
*/
const Person = S.Struct({
name: S.String,
age: S.propertySignature(S.NumberFromString).pipe(S.fromKey("AGE"))
})
This modification is represented in the type of the created PropertySignature:
// fromKey ----------------------v
PropertySignature<":", number, "AGE", ":", string, never>
Now, let's see an example of decoding:
console.log(S.decodeUnknownSync(Person)({ name: "name", AGE: "18" }))
// Output: { name: 'name', age: 18 }
Cheatsheet
| Combinator | From | To |
|---|---|---|
optional | Schema<A, I, R> | PropertySignature<"?:", string | undefined, never, "?:", string | undefined, never> |
optional | Schema<A, I, R>, { nullable: true } | PropertySignature<"?:", string | null | undefined, never, "?:", string | null | undefined, never> |
optional | Schema<A, I, R>, { exact: true } | PropertySignature<"?:", string, never, "?:", string, never> |
optional | Schema<A, I, R>, { exact: true, nullable: true } | PropertySignature<"?:", string | null, never, "?:", string | null, never> |
<missing value> -> <missing value>undefined -> undefinedi -> a<missing value> -> <missing value>undefined -> undefineda -> i<missing value> -> <missing value>undefined -> undefinednull -> <missing value>i -> a<missing value> -> <missing value>undefined -> undefineda -> i<missing value> -> <missing value>i -> a<missing value> -> <missing value>a -> i<missing value> -> <missing value>null -> <missing value>i -> a<missing value> -> <missing value>a -> i| Combinator | From | To |
|---|---|---|
optional | Schema<A, I, R>, { default: () => A } | PropertySignature<":", string, never, "?:", string | undefined, never> |
optional | Schema<A, I, R>, { exact: true, default: () => A } | PropertySignature<":", string, never, "?:", string, never> |
optional | Schema<A, I, R>, { nullable: true, default: () => A } | PropertySignature<":", string, never, "?:", string | null | undefined, never> |
optional | Schema<A, I, R>, { exact: true, nullable: true, default: () => A } | PropertySignature<":", string, never, "?:", string | null, never> |
<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<":", Option<string>, never, "?:", string | undefined, never> |
optional | Schema<A, I, R>, { exact: true, as: "Option" } | PropertySignature<":", Option<string>, never, "?:", string, never> |
optional | Schema<A, I, R>, { nullable: true, as: "Option" } | PropertySignature<":", Option<string>, never, "?:", string | null | undefined, never> |
optional | Schema<A, I, R>, { exact: true, nullable: true, as: "Option" } | PropertySignature<":", Option<string>, never, "?:", string | null, never> |
<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) -> iThe optional API is based on two primitives: pptionalToOptional and optionalTorequired. These primitives are incredibly useful for defining property signatures with more precision.
The pptionalToOptional API is used to manage the transformation from an optional field to another optional field. With this, we can control both the output type and the presence or absence of the field.
For example a common use case is to equate a specific value in the source field with the absence of value in the destination field.
Here's the signature of the pptionalToOptional API:
export const optionalToOptional = <FA, FI, FR, TA, TI, TR>(
from: Schema<FA, FI, FR>,
to: Schema<TA, TI, TR>,
options: {
readonly decode: (o: Option.Option<FA>) => Option.Option<TI>,
readonly encode: (o: Option.Option<TI>) => Option.Option<FA>
}
): PropertySignature<"?:", TA, never, "?:", FI, FR | TR>
As you can see, we can transform the type by specifying a schema for to, which can be different from the schema of from. Additionally, we can control the presence or absence of the field using decode and encode, with the following meanings:
decode:
none as an argument means the value is missing in the inputnone as a return value means the value will be missing in the outputencode:
none as an argument means the value is missing in the inputnone as a return value means the value will be missing in the outputExample
Suppose we have an optional field of type string, and we want to exclude empty strings from the output. In other words, if the input contains an empty string, we want the field to be absent in the output.
import * as S from "@effect/schema/Schema"
import { identity } from "effect/Function"
import * as Option from "effect/Option"
const schema = S.Struct({
a: S.optionalToOptional(S.String, S.String, {
decode: (input) => {
if (Option.isNone(input)) {
// If the field is absent in the input, returning `Option.none()` will make it absent in the output too
return Option.none()
}
const value = input.value
if (value === "") {
// If the field is present in the input but is an empty string, returning `Option.none()` will make it absent in the output
return Option.none()
}
// If the field is present in the input and is not an empty string, returning `Option.some` will make it present in the output
return Option.some(value)
},
// Here in the encoding part, we can decide to handle things in the same way as in the decoding phase
// or handle them differently. For example, we can leave everything unchanged and use the identity function
encode: identity
})
})
const decode = S.decodeUnknownSync(schema)
console.log(decode({})) // Output: {}
console.log(decode({ a: "" })) // Output: {}
console.log(decode({ a: "a non-empty string" })) // Output: { a: 'a non-empty string' }
const encode = S.encodeSync(schema)
console.log(encode({})) // Output: {}
console.log(encode({ a: "" })) // Output: { a: '' }
console.log(encode({ a: "foo" })) // Output: { a: 'foo' }
The optionalTorequired API allows us to transform an optional field into a required one, applying custom logic if the field is absent in the input.
export const optionalTorequired = <FA, FI, FR, TA, TI, TR>(
from: Schema<FA, FI, FR>,
to: Schema<TA, TI, TR>,
options: {
readonly decode: (o: Option.Option<FA>) => TI,
readonly encode: (ti: TI) => Option.Option<FA>
}
): PropertySignature<":", TA, never, "?:", FI, FR | TR>
For instance, a common use case is to assign a default value to the field in the output if it's missing in the input. Let's see an example:
import * as S from "@effect/schema/Schema"
import * as Option from "effect/Option"
const schema = S.Struct({
a: S.optionalTorequired(S.String, S.String, {
decode: (input) => {
if (Option.isNone(input)) {
// If the field is absent in the input, we can return the default value for the field in the output
return "default value"
}
// If the field is present in the input, return its value as it is in the output
return input.value
},
// During encoding, we can choose to handle things differently, or simply return the same value present in the input for the output
encode: (a) => Option.some(a)
})
})
const decode = S.decodeUnknownSync(schema)
console.log(decode({})) // Output: { a: 'default value' }
console.log(decode({ a: "foo" })) // Output: { a: 'foo' }
const encode = S.encodeSync(schema)
console.log(encode({ a: "foo" })) // Output: { a: 'foo' }
import * as S from "@effect/schema/Schema"
const schema = S.Struct({
a: S.propertySignature(S.String).pipe(S.fromKey("c")),
b: S.Number
})
console.log(S.decodeUnknownSync(schema)({ c: "c", b: 1 }))
// Output: { a: "c", b: 1 }
To rename one or more properties, you can utilize the rename API:
import * as S from "@effect/schema/Schema"
// Original Schema
const originalSchema = S.Struct({ c: S.String, b: S.Number })
// Renaming the "a" property to "c"
const renamedSchema = S.rename(originalSchema, { c: "a" })
console.log(S.decodeUnknownSync(renamedSchema)({ c: "c", b: 1 }))
// Output: { a: "c", 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.
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.
By default, the partial operation adds a union with undefined to the types. If you wish to avoid this, you can opt-out by passing a { exact: true } argument to the partial operation.
Example
import * as S from "@effect/schema/Schema"
/*
const schema: S.Schema<{
readonly a?: string | undefined;
}, {
readonly a?: string | undefined;
}, never>
*/
const schema = S.partial(S.Struct({ a: S.String }))
S.decodeUnknownSync(schema)({ a: "a" }) // ok
S.decodeUnknownSync(schema)({ a: undefined }) // ok
/*
const exact: S.Schema<{
readonly a?: string;
}, {
readonly a?: string;
}, never>
*/
const exactSchema = S.partial(S.Struct({ a: S.String }), { exact: true })
S.decodeUnknownSync(exactSchema)({ a: "a" }) // ok
S.decodeUnknownSync(exactSchema)({ a: undefined })
/*
throws:
Error: { a?: string }
└─ ["a"]
└─ Expected a string, actual undefined
*/
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 })
})
)
The extend combinator allows you to add additional fields or index signatures to an existing Schema.
import * as S from "@effect/schema/Schema"
const schema = S.Struct({ a: S.String, b: S.String })
// Schema<{ readonly [x: string]: string; readonly a: string; readonly b: string; readonly c: string; }>
const extended = schema.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
)
Alternatively, you can utilize the fields property of structs:
import * as S from "@effect/schema/Schema"
const schema = S.Struct({ a: S.String, b: S.String })
// Schema<{ readonly [x: string]: string; readonly a: string; readonly b: string; readonly c: string; }>
const extended = S.Struct(
{
...schema.fields,
c: S.String
},
{ key: S.String, value: S.String }
)
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>,
options: { readonly strict: false } // Less strict constraint
) => 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))
})
[!NOTE] It is necessary to define the
Categorytype and add an explicit type annotation (const Category: S.Schema<Category>) because otherwise TypeScript would struggle to infer types correctly. Without this annotation, you might encounter the error message: "'Category' implicitly has type 'any' because it does not have a type annotation and is referenced directly or indirectly in its own initializer.ts(7022)"
As we've observed, it's necessary to define an interface for the Type of the schema to enable recursive schema definition, which can complicate things and be quite tedious. One pattern to mitigate this is to separate the field responsible for recursion from all other fields.
import * as S from "@effect/schema/Schema"
const fields = {
name: S.String
// ...possibly other fields
}
// Define an interface for the Category schema, extending the Type of the defined fields
interface Category extends S.Struct.Type<typeof fields> {
readonly subcategories: ReadonlyArray<Category> // Define `subcategories` using recursion
}
const Category: S.Schema<Category> = S.Struct({
...fields, // Include the fields
subcategories: S.Array(S.suspend(() => Category)) // Define `subcategories` using recursion
})
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.Literal("+", "-"),
left: Expression,
right: Expression
})
Defining a recursive schema where the Encoded type differs from the Type type adds another layer of complexity. In such cases, we need to define two interfaces: one for the Type type, as seen previously, and another for the Encoded type.
Let's consider an example: suppose we want to add an id field to the Category schema, where the schema for id is NumberFromString. It's important to note that NumberFromString is a schema that transforms a string into a number, so the Type and Encoded types of NumberFromString differ, being number and string respectively. When we add this field to the Category schema, TypeScript raises an error:
import * as S from "@effect/schema/Schema"
const fields = {
id: S.NumberFromString,
name: S.String
}
interface Category extends S.Struct.Type<typeof fields> {
readonly subcategories: ReadonlyArray<Category>
}
/*
TypeScript error:
Type 'Category' is not assignable to type '{ readonly id: string; readonly name: string; readonly subcategories: readonly Category[]; }'.
Types of property 'id' are incompatible.
Type 'number' is not assignable to type 'string'.ts(2322)
*/
const Category: S.Schema<Category> = S.Struct({
...fields,
subcategories: S.Array(S.suspend(() => Category))
})
This error occurs because the explicit annotation const Category: S.Schema<Category> is no longer sufficient and needs to be adjusted by explicitly adding the Encoded type:
import * as S from "@effect/schema/Schema"
const fields = {
id: S.NumberFromString,
name: S.String
}
interface Category extends S.Struct.Type<typeof fields> {
readonly subcategories: ReadonlyArray<Category>
}
interface CategoryEncoded extends S.Struct.Encoded<typeof fields> {
readonly subcategories: ReadonlyArray<CategoryEncoded>
}
const Category: S.Schema<Category, CategoryEncoded> = S.Struct({
...fields,
subcategories: S.Array(S.suspend(() => Category))
})
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>("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 ""
*/
...unless you explicitly disable validation:
new Person({ id: 1, name: "" }, true) // no error
If you don't want to have any arguments, you can use {}:
import * as S from "@effect/schema/Schema"
class NoArgs extends S.Class<NoArgs>("NoArgs")({}) {}
const noargs = new NoArgs()
For more flexibility, you can also introduce custom getters and methods:
import * as S from "@effect/schema/Schema"
class Person extends S.Class<Person>("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 .fields 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>("Person")({
id: S.Number,
name: S.String.pipe(S.nonEmpty())
}) {}
console.log(S.isSchema(Person)) // true
/*
{
readonly id: S.$Number;
readonly name: S.Schema<string, string, never>;
}
*/
Person.fields
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"
class Category extends S.Class<Category>("Category")({
name: S.String,
subcategories: S.Array(S.suspend((): S.Schema<Category> => Category))
}) {}
[!NOTE] It is necessary to add an explicit type annotation (
S.suspend((): S.Schema<Category> => Category) because otherwise TypeScript would struggle to infer types correctly. Without this annotation, you might encounter the error message: "Type 'typeof Category' is missing the following properties from type 'Schema<unknown, unknown, unknown>': ast, annotations, [TypeId], pipets(2739)"
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"
class Expression extends S.Class<Expression>("Expression")({
type: S.Literal("expression"),
value: S.Union(
S.Number,
S.suspend((): S.Schema<Operation> => Operation)
)
}) {}
class Operation extends S.Class<Operation>("Operation")({
type: S.Literal("operation"),
operator: S.Literal("+", "-"),
left: Expression,
right: Expression
}) {}
Defining a recursive schema where the Encoded type differs from the Type type adds another layer of complexity. In such cases, we need to define an interface for the Encoded type.
Let's consider an example: suppose we want to add an id field to the Category schema, where the schema for id is NumberFromString. It's important to note that NumberFromString is a schema that transforms a string into a number, so the Type and Encoded types of NumberFromString differ, being number and string respectively. When we add this field to the Category schema, TypeScript raises an error:
import * as S from "@effect/schema/Schema"
/*
TypeScript error:
Type 'Category' is not assignable to type '{ readonly id: string; readonly name: string; readonly subcategories: readonly Category[]; }'.
Types of property 'id' are incompatible.
Type 'number' is not assignable to type 'string'.ts(2322)
*/
class Category extends S.Class<Category>("Category")({
id: S.NumberFromString,
name: S.String,
subcategories: S.Array(S.suspend((): S.Schema<Category> => Category))
}) {}
This error occurs because the explicit annotation S.suspend((): S.Schema<Category> => Category is no longer sufficient and needs to be adjusted by explicitly adding the Encoded type:
import * as S from "@effect/schema/Schema"
interface CategoryEncoded {
readonly id: string
readonly name: string
readonly subcategories: ReadonlyArray<CategoryEncoded>
}
class Category extends S.Class<Category>("Category")({
id: S.NumberFromString,
name: S.String,
subcategories: S.Array(
S.suspend((): S.Schema<Category, CategoryEncoded> => Category)
)
}) {}
As we've observed, it's necessary to define an interface for the Encoded of the schema to enable recursive schema definition, which can complicate things and be quite tedious. One pattern to mitigate this is to separate the field responsible for recursion from all other fields.
import * as S from "@effect/schema/Schema"
const fields = {
id: S.NumberFromString,
name: S.String
// ...possibly other fields
}
interface CategoryEncoded extends S.Struct.Encoded<typeof fields> {
readonly subcategories: ReadonlyArray<CategoryEncoded> // Define `subcategories` using recursion
}
class Category extends S.Class<Category>("Category")({
...fields, // Include the fields
subcategories: S.Array(
S.suspend((): S.Schema<Category, CategoryEncoded> => Category)
) // Define `subcategories` using recursion
}) {}
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>("Person")({
id: S.Number,
name: S.String.pipe(S.nonEmpty())
}) {
get upperName() {
return this.name.toUpperCase()
}
}
class PersonWithAge extends Person.extend<PersonWithAge>("PersonWithAge")({
age: S.Number
}) {
get isAdult() {
return this.age >= 18
}
}
You have the option to enhance a class with (effectful) transformations. 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>("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<number, Error> {
return Effect.succeed(id + 2)
}
export class PersonWithTransform extends Person.transformOrFail<PersonWithTransform>(
"PersonWithTransform"
)(
{
age: S.optional(S.Number, { exact: true, as: "Option" })
},
{
decode: (input) =>
Effect.mapBoth(getAge(input.id), {
onFailure: (e) =>
new ParseResult.Type(S.String.ast, input.id, e.message),
// must return { age: Option<number> }
onSuccess: (age) => ({ ...input, age: Option.some(age) })
}),
encode: 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>(
"PersonWithTransformFrom"
)(
{
age: S.optional(S.Number, { exact: true, as: "Option" })
},
{
decode: (input) =>
Effect.mapBoth(getAge(input.id), {
onFailure: (e) => new ParseResult.Type(S.String.ast, input, e.message),
// must return { age?: number }
onSuccess: (age) => (age > 18 ? { ...input, age } : { ...input })
}),
encode: 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 transformOrFail or transformOrFailFrom, depends on when you wish to execute the transformation:
Using transformOrFail:
{ age: Option<number> }.Using transformOrFailFrom:
{ age?: number }.{ age: S.optionalToOption(S.Number, { exact: true }) } is executed.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: <To extends Schema.Any, From extends Schema.Any>(
from: From,
to: To,
options: {
readonly decode: (fromA: Schema.Type<From>) => Schema.Encoded<To>
readonly encode: (toI: Schema.Encoded<To>) => Schema.Type<From>
readonly strict?: true
} | {
readonly decode: (fromA: Schema.Type<From>) => unknown
readonly encode: (toI: Schema.Encoded<To>) => unknown
readonly strict: false
}
): transform<From, To>
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
decode: (s) => [s] as const,
// define a function that converts a tuple with one element of type string into a string
encode: ([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:
<To extends Schema.Any, From extends Schema.Any>(
from: From,
to: To,
options: {
readonly decode: (fromA: Schema.Type<From>) => Schema.Encoded<To>
readonly encode: (toI: Schema.Encoded<To>) => Schema.Type<From>
readonly strict?: true
} | {
readonly decode: (fromA: Schema.Type<From>) => unknown // Less strict constraint
readonly encode: (toI: Schema.Encoded<To>) => unknown // Less strict constraint
readonly strict: false
}
): transform<From, To>
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<A, ParseError, R, which can either be a success or a failure.
<To extends Schema.Any, From extends Schema.Any, RD, RE>(
from: From,
to: To,
options: {
readonly decode: (
fromA: Schema.Type<From>,
options: ParseOptions,
ast: AST.Transformation
) => Effect.Effect<Schema.Encoded<To>, ParseResult.ParseIssue, RD>
readonly encode: (
toI: Schema.Encoded<To>,
options: ParseOptions,
ast: AST.Transformation
) => Effect.Effect<Schema.Type<From>, ParseResult.ParseIssue, RE>
readonly strict?: true
} | {
readonly decode: (
fromA: Schema.Type<From>,
options: ParseOptions,
ast: AST.Transformation
) => Effect.Effect<unknown, ParseResult.ParseIssue, RD>
readonly encode: (
toI: Schema.Encoded<To>,
options: ParseOptions,
ast: AST.Transformation
) => Effect.Effect<unknown, ParseResult.ParseIssue, RE>
readonly strict: false
}
): transformOrFail<From, To, RD | RE>
Both decode and encode functions not only receive the value to transform (fromA and toI), but also the parse options that the user sets when using the resulting schema, and the ast, which represents the AST of the schema you're transforming.
Example
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
decode: (s) =>
s === "true"
? ParseResult.succeed(true)
: s === "false"
? ParseResult.succeed(false)
: ParseResult.fail(
new ParseResult.Type(S.Literal("true", "false").ast, s)
),
// define a function that converts a boolean into a string
encode: (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<unknown, Error> =>
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, {
decode: (s, _, ast) =>
Effect.mapBoth(api(`https://swapi.dev/api/people/${s}`), {
onFailure: (e) => new ParseResult.Type(ast, s, e.message),
onSuccess: () => s
}),
encode: ParseResult.succeed
})
const decode = (id: string) =>
Effect.mapError(S.decodeUnknown(PeopleIdFromString)(id), (e) =>
TreeFormatter.formatError(e)
)
Effect.runPromiseExit(decode("1")).then(console.log)
/*
Output:
{ _id: 'Exit', _tag: 'Success', value: '1' }
*/
Effect.runPromiseExit(decode("fail")).then(console.log)
/*
Output:
{
_id: 'Exit',
_tag: 'Failure',
cause: {
_id: 'Cause',
_tag: 'Fail',
failure: '(string <-> string)\n└─ Transformation process failure\n └─ Error: 404'
}
}
*/
You can also declare dependencies:
import * as ParseResult from "@effect/schema/ParseResult"
import * as S from "@effect/schema/Schema"
import * as TreeFormatter from "@effect/schema/TreeFormatter"
import * as Context from "effect/Context"
import * as Effect from "effect/Effect"
import * as Layer from "effect/Layer"
const Fetch = Context.GenericTag<"Fetch", typeof fetch>("Fetch")
const api = (url: string): Effect.Effect<unknown, Error, "Fetch"> =>
Fetch.pipe(
Effect.flatMap((fetch) =>
Effect.tryPromise({
try: () =>
fetch(url).then((res) => {
if (res.ok) {
return res.json() as Promise<unknown>
}
throw new Error(String(res.status))
}),
catch: (e) => new Error(String(e))
})
)
)
const PeopleId = S.String.pipe(S.brand("PeopleId"))
const PeopleIdFromString = S.transformOrFail(S.String, PeopleId, {
decode: (s, _, ast) =>
Effect.mapBoth(api(`https://swapi.dev/api/people/${s}`), {
onFailure: (e) => new ParseResult.Type(ast, s, e.message),
onSuccess: () => s
}),
encode: ParseResult.succeed
})
const decode = (id: string) =>
Effect.mapError(S.decodeUnknown(PeopleIdFromString)(id), (e) =>
TreeFormatter.formatError(e)
)
const FetchLive = Layer.succeed(Fetch, fetch)
Effect.runPromiseExit(decode("1").pipe(Effect.provide(FetchLive))).then(
console.log
)
/*
Output:
{ _id: 'Exit', _tag: 'Success', value: '1' }
*/
Effect.runPromiseExit(decode("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 decode = S.decodeUnknownSync(schema)
console.log(decode("")) // [""]
console.log(decode(",")) // ["", ""]
console.log(decode("a,")) // ["a", ""]
console.log(decode("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 decode = S.decodeUnknownSync(schema)
console.log(decode("a")) // "a"
console.log(decode(" a")) // "a"
console.log(decode("a ")) // "a"
console.log(decode(" a ")) // "a"
Note. If you were looking for a combinator to check if a string is trimmed, check out the trimmed filter.
The Lowercase schema converts a string to lowercase.
import * as S from "@effect/schema/Schema"
const decode = S.decodeUnknownSync(S.Lowercase)
console.log(decode("A")) // "a"
console.log(decode(" AB")) // " ab"
console.log(decode("Ab ")) // "ab "
console.log(decode(" ABc ")) // " abc "
Note. If you were looking for a combinator to check if a string is lowercased, check out the Lowercased schema or the lowercased filter.
The Uppercase schema converts a string to uppercase.
import * as S from "@effect/schema/Schema"
const decode = S.decodeUnknownSync(S.Uppercase)
console.log(decode("a")) // "A"
console.log(decode(" ab")) // " AB"
console.log(decode("aB ")) // "AB "
console.log(decode(" abC ")) // " ABC "
Note. If you were looking for a combinator to check if a string is uppercased, check out the Uppercased schema or the uppercased filter.
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 decode = S.decodeUnknownSync(schema)
// Parse valid JSON strings
console.log(decode("{}")) // Output: {}
console.log(decode(`{"a":"b"}`)) // Output: { a: "b" }
// Attempting to decode an empty string results in an error
decode("")
/*
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 decode = S.decodeUnknownSync(schema)
// success cases
console.log(decode("1")) // 1
console.log(decode("-1")) // -1
console.log(decode("1.5")) // 1.5
console.log(decode("NaN")) // NaN
console.log(decode("Infinity")) // Infinity
console.log(decode("-Infinity")) // -Infinity
// failure cases
decode("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 decode = S.decodeUnknownSync(schema)
console.log(decode(-3)) // -1
console.log(decode(0)) // 0
console.log(decode(3)) // 1
Negates a boolean value.
import * as S from "@effect/schema/Schema"
// Schema<boolean>
const schema = S.Not
const decode = S.decodeUnknownSync(schema)
console.log(decode(true)) // false
console.log(decode(false)) // true
Transforms a string into a symbol by parsing the string using Symbol.for.
import * as S from "@effect/schema/Schema"
const schema = S.Symbol // Schema<symbol, string>
const decode = S.decodeUnknownSync(schema)
console.log(decode("a")) // Symbol(a)
Transforms a string into a BigInt by parsing the string using BigInt.
import * as S from "@effect/schema/Schema"
const schema = S.BigInt // Schema<BigInt, string>
const decode = S.decodeUnknownSync(schema)
// success cases
console.log(decode("1")) // 1n
console.log(decode("-1")) // -1n
// failure cases
decode("a")
/*
throws:
Error: BigInt
└─ Transformation process failure
└─ Expected BigInt, actual "a"
*/
decode("1.5") // throws
decode("NaN") // throws
decode("Infinity") // throws
decode("-Infinity") // throws
Transforms a number into a BigInt by parsing the number using BigInt.
import * as S from "@effect/schema/Schema"
const schema = S.BigIntFromNumber // Schema<BigInt, number>
const decode = S.decodeUnknownSync(schema)
const encode = S.encodeSync(schema)
// success cases
console.log(decode(1)) // 1n
console.log(decode(-1)) // -1n
console.log(encode(1n)) // 1
console.log(encode(-1n)) // -1
// failure cases
decode(1.5)
/*
throws:
Error: BigIntFromNumber
└─ Transformation process failure
└─ Expected BigIntFromNumber, actual 1.5
*/
decode(NaN) // throws
decode(Infinity) // throws
decode(-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"
const schema = S.BigIntFromSelf.pipe(S.clampBigInt(-1n, 1n)) // clamps the input to -1n <= x <= 1n
const decode = S.decodeUnknownSync(schema)
console.log(decode(-3n)) // -1n
console.log(decode(0n)) // 0n
console.log(decode(3n)) // 1n
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"
const schema = S.Date // Schema<Date, string>
const decode = S.decodeUnknownSync(schema)
console.log(decode("1970-01-01T00:00:00.000Z")) // 1970-01-01T00:00:00.000Z
decode("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
*/
Transforms a string into a BigDecimal.
import * as S from "@effect/schema/Schema"
const schema = S.BigDecimal // Schema<BigDecimal, string>
const decode = S.decodeUnknownSync(schema)
console.log(decode(".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"
const schema = S.BigDecimalFromNumber // Schema<BigDecimal, number>
const decode = S.decodeUnknownSync(schema)
console.log(decode(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"
const schema = S.BigDecimal.pipe(
S.clampBigDecimal(BigDecimal.fromNumber(-1), BigDecimal.fromNumber(1))
)
const decode = S.decodeUnknownSync(schema)
console.log(decode("-2")) // { _id: 'BigDecimal', value: '-1', scale: 0 }
console.log(decode("0")) // { _id: 'BigDecimal', value: '0', scale: 0 }
console.log(decode("3")) // { _id: 'BigDecimal', value: '1', scale: 0 }
Converts an hrtime(i.e. [seconds: number, nanos: number]) into a Duration.
import * as S from "@effect/schema/Schema"
const schema = S.Duration // Schema<Duration, number>
const decode = S.decodeUnknownSync(schema)
console.log(decode([0, 0])) // { _id: 'Duration', _tag: 'Nanos', hrtime: [ 0, 0 ] }
console.log(decode([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"
const schema = S.DurationFromMillis // Schema<Duration, number>
const decode = S.decodeUnknownSync(schema)
console.log(decode(0)) // { _id: 'Duration', _tag: 'Millis', millis: 0 }
console.log(decode(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"
const schema = S.DurationFromNanos // Schema<Duration, BigInt>
const decode = S.decodeUnknownSync(schema)
console.log(decode(0n)) // { _id: 'Duration', _tag: 'Nanos', hrtime: [ 0, 0 ] }
console.log(decode(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"
const schema = S.DurationFromSelf.pipe(
S.clampDuration("5 seconds", "10 seconds")
)
const decode = S.decodeUnknownSync(schema)
console.log(decode(Duration.decode("2 seconds"))) // { _id: 'Duration', _tag: 'Millis', millis: 5000 }
console.log(decode(Duration.decode("6 seconds"))) // { _id: 'Duration', _tag: 'Millis', millis: 6000 }
console.log(decode(Duration.decode("11 seconds"))) // { _id: 'Duration', _tag: 'Millis', millis: 10000 }
Converts a string into a Secret.
import * as S from "@effect/schema/Schema"
const schema = S.Secret // Schema<Secret, string>
const decode = S.decodeUnknownSync(schema)
console.log(decode("keep it secret, keep it safe")) // {}
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 with Equal and Hash traits added:
import * as S from "@effect/schema/Schema"
import * as Equal from "effect/Equal"
/*
Schema<{
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> |
OptionFromNullOr | Schema<A, I, R> | Schema<Option<A>, I | null, R> |
OptionFromNullishOr | 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 }import * as S from "@effect/schema/Schema"
import * as Option from "effect/Option"
const schema = S.Option(S.NumberFromString)
const decode = S.decodeUnknownSync(schema)
const encode = S.encodeSync(schema)
console.log(decode({ _tag: "None" })) // Option.none()
console.log(decode({ _tag: "Some", value: "1" })) // Option.some(1)
console.log(encode(Option.none())) // { _tag: 'None' }
console.log(encode(Option.some(1))) // { _tag: 'Some', value: '1' }
Option.none() -> Option.none()Option.some(i) -> Option.some(a)Option.none() -> Option.none()Option.some(a) -> Option.some(i)import * as S from "@effect/schema/Schema"
import * as Option from "effect/Option"
const schema = S.OptionFromSelf(S.NumberFromString)
const decode = S.decodeUnknownSync(schema)
const encode = S.encodeSync(schema)
console.log(decode(Option.none())) // Option.none()
console.log(decode(Option.some("1"))) // Option.some(1)
console.log(encode(Option.none())) // Option.none()
console.log(encode(Option.some(1))) // Option.some("1")
null -> Option.none()i -> Option.some(a)Option.none() -> nullOption.some(a) -> iimport * as S from "@effect/schema/Schema"
import * as Option from "effect/Option"
const schema = S.OptionFromNullOr(S.NumberFromString)
const decode = S.decodeUnknownSync(schema)
const encode = S.encodeSync(schema)
console.log(decode(null)) // Option.none()
console.log(decode("1")) // Option.some(1)
console.log(encode(Option.none())) // null
console.log(encode(Option.some(1))) // "1"
null -> Option.none()undefined -> Option.none()i -> Option.some(a)Option.none() -> <onNoneEncoding value>Option.some(a) -> iimport * as S from "@effect/schema/Schema"
import * as Option from "effect/Option"
const schema = S.OptionFromNullishOr(S.NumberFromString, undefined)
const decode = S.decodeUnknownSync(schema)
const encode = S.encodeSync(schema)
console.log(decode(null)) // Option.none()
console.log(decode(undefined)) // Option.none()
console.log(decode("1")) // Option.some(1)
console.log(encode(Option.none())) // undefined
console.log(encode(Option.some(1))) // "1"
{ _tag: "Left", left: li } -> Either.left(la){ _tag: "Right", right: ri } -> Either.right(ra)Either.left(la) -> { _tag: "Left", left: li }Either.right(ra) -> { _tag: "Right", right: ri }import * as S from "@effect/schema/Schema"
import * as Either from "effect/Either"
const schema = S.Either({ left: S.Trim, right: S.NumberFromString })
const decode = S.decodeUnknownSync(schema)
const encode = S.encodeSync(schema)
console.log(decode({ _tag: "Left", left: " a " })) // Either.left("a")
console.log(decode({ _tag: "Right", right: "1" })) // Either.right(1)
console.log(encode(Either.left("a"))) // { _tag: 'Left', left: 'a' }
console.log(encode(Either.right(1))) // { _tag: 'Right', right: '1' }
Either.left(li) -> Either.left(la)Either.right(ri) -> Either.right(ra)Either.left(la) -> Either.left(li)Either.right(ra) -> Either.right(ri)import * as S from "@effect/schema/Schema"
import * as Either from "effect/Either"
const schema = S.EitherFromSelf({ left: S.Trim, right: S.NumberFromString })
const decode = S.decodeUnknownSync(schema)
const encode = S.encodeSync(schema)
console.log(decode(Either.left(" a "))) // Either.left("a")
console.log(decode(Either.right("1"))) // Either.right(1)
console.log(encode(Either.left("a"))) // Either.left("a")
console.log(encode(Either.right(1))) // Either.right("1")
li -> Either.left(la)ri -> Either.right(ra)Either.left(la) -> liEither.right(ra) -> riimport * as S from "@effect/schema/Schema"
import * as Either from "effect/Either"
const schema = S.EitherFromUnion({
left: S.Boolean,
right: S.NumberFromString
})
const decode = S.decodeUnknownSync(schema)
const encode = S.encodeSync(schema)
console.log(decode(true)) // Either.left(true)
console.log(decode("1")) // Either.right(1)
console.log(encode(Either.left(true))) // true
console.log(encode(Either.right(1))) // "1"
ReadonlyArray<I> -> ReadonlySet<A>ReadonlySet<A> -> ReadonlyArray<I>import * as S from "@effect/schema/Schema"
const schema = S.ReadonlySet(S.NumberFromString)
const decode = S.decodeUnknownSync(schema)
const encode = S.encodeSync(schema)
console.log(decode(["1", "2", "3"])) // Set(3) { 1, 2, 3 }
console.log(encode(new Set([1, 2, 3]))) // [ '1', '2', '3' ]
ReadonlySet<I> -> ReadonlySet<A>ReadonlySet<A> -> ReadonlySet<I>import * as S from "@effect/schema/Schema"
const schema = S.ReadonlySetFromSelf(S.NumberFromString)
const decode = S.decodeUnknownSync(schema)
const encode = S.encodeSync(schema)
console.log(decode(new Set(["1", "2", "3"]))) // Set(3) { 1, 2, 3 }
console.log(encode(new Set([1, 2, 3]))) // Set(3) { '1', '2', '3' }
ReadonlyArray<readonly [KI, VI]> -> ReadonlyMap<KA, VA>ReadonlyMap<KA, VA> -> ReadonlyArray<readonly [KI, VI]>import * as S from "@effect/schema/Schema"
const schema = S.ReadonlyMap({ key: S.String, value: S.NumberFromString })
const decode = S.decodeUnknownSync(schema)
const encode = S.encodeSync(schema)
console.log(
decode([
["a", "2"],
["b", "2"],
["c", "3"]
])
) // Map(3) { 'a' => 2, 'b' => 2, 'c' => 3 }
console.log(
encode(
new Map([
["a", 1],
["b", 2],
["c", 3]
])
)
) // [ [ 'a', '1' ], [ 'b', '2' ], [ 'c', '3' ] ]
ReadonlyMap<KI, VI> -> ReadonlyMap<KA, VA>ReadonlyMap<KA, VA> -> ReadonlyMap<KI, VI>import * as S from "@effect/schema/Schema"
const schema = S.ReadonlyMapFromSelf({
key: S.String,
value: S.NumberFromString
})
const decode = S.decodeUnknownSync(schema)
const encode = S.encodeSync(schema)
console.log(
decode(
new Map([
["a", "2"],
["b", "2"],
["c", "3"]
])
)
) // Map(3) { 'a' => 2, 'b' => 2, 'c' => 3 }
console.log(
encode(
new Map([
["a", 1],
["b", 2],
["c", 3]
])
)
) // Map(3) { 'a' => '1', 'b' => '2', 'c' => '3' }
ReadonlyArray<I> -> HashSet<A>HashSet<A> -> ReadonlyArray<I>import * as S from "@effect/schema/Schema"
import * as HashSet from "effect/HashSet"
const schema = S.HashSet(S.NumberFromString)
const decode = S.decodeUnknownSync(schema)
const encode = S.encodeSync(schema)
console.log(decode(["1", "2", "3"])) // { _id: 'HashSet', values: [ 1, 2, 3 ] }
console.log(encode(HashSet.frOmIterable([1, 2, 3]))) // [ '1', '2', '3' ]
HashSet<I> -> HashSet<A>HashSet<A> -> HashSet<I>import * as S from "@effect/schema/Schema"
import * as HashSet from "effect/HashSet"
const schema = S.HashSetFromSelf(S.NumberFromString)
const decode = S.decodeUnknownSync(schema)
const encode = S.encodeSync(schema)
console.log(decode(HashSet.frOmIterable(["1", "2", "3"]))) // { _id: 'HashSet', values: [ 1, 2, 3 ] }
console.log(encode(HashSet.frOmIterable([1, 2, 3]))) // { _id: 'HashSet', values: [ '1', '3', '2' ] }
ReadonlyArray<readonly [KI, VI]> -> HashMap<KA, VA>HashMap<KA, VA> -> ReadonlyArray<readonly [KI, VI]>import * as S from "@effect/schema/Schema"
import * as HashMap from "effect/HashMap"
const schema = S.HashMap({ key: S.String, value: S.NumberFromString })
const decode = S.decodeUnknownSync(schema)
const encode = S.encodeSync(schema)
console.log(
decode([
["a", "2"],
["b", "2"],
["c", "3"]
])
) // { _id: 'HashMap', values: [ [ 'a', 2 ], [ 'c', 3 ], [ 'b', 2 ] ] }
console.log(
encode(
HashMap.frOmIterable([
["a", 1],
["b", 2],
["c", 3]
])
)
) // [ [ 'a', '1' ], [ 'c', '3' ], [ 'b', '2' ] ]
HashMap<KI, VI> -> HashMap<KA, VA>HashMap<KA, VA> -> HashMap<KI, VI>import * as S from "@effect/schema/Schema"
import * as HashMap from "effect/HashMap"
const schema = S.HashMapFromSelf({ key: S.String, value: S.NumberFromString })
const decode = S.decodeUnknownSync(schema)
const encode = S.encodeSync(schema)
console.log(
decode(
HashMap.frOmIterable([
["a", "2"],
["b", "2"],
["c", "3"]
])
)
) // { _id: 'HashMap', values: [ [ 'a', 2 ], [ 'c', 3 ], [ 'b', 2 ] ] }
console.log(
encode(
HashMap.frOmIterable([
["a", 1],
["b", 2],
["c", 3]
])
)
) // { _id: 'HashMap', values: [ [ 'a', '1' ], [ 'c', '3' ], [ 'b', '2' ] ] }
ReadonlyArray<I> -> SortedSet<A>SortedSet<A> -> ReadonlyArray<I>import * as S from "@effect/schema/Schema"
import * as N from "effect/Number"
import * as SortedSet from "effect/SortedSet"
const schema = S.SortedSet(S.NumberFromString, N.Order)
const decode = S.decodeUnknownSync(schema)
const encode = S.encodeSync(schema)
console.log(decode(["1", "2", "3"])) // { _id: 'SortedSet', values: [ 1, 2, 3 ] }
console.log(encode(SortedSet.frOmIterable(N.Order)([1, 2, 3]))) // [ '1', '2', '3' ]
SortedSet<I> -> SortedSet<A>SortedSet<A> -> SortedSet<I>import * as S from "@effect/schema/Schema"
import * as N from "effect/Number"
import * as SortedSet from "effect/SortedSet"
import * as Str from "effect/String"
const schema = S.SortedSetFromSelf(S.NumberFromString, N.Order, Str.Order)
const decode = S.decodeUnknownSync(schema)
const encode = S.encodeSync(schema)
console.log(decode(SortedSet.frOmIterable(Str.Order)(["1", "2", "3"]))) // { _id: 'SortedSet', values: [ 1, 2, 3 ] }
console.log(encode(SortedSet.frOmIterable(N.Order)([1, 2, 3]))) // { _id: 'SortedSet', values: [ '1', '2', '3' ] }
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 decode = S.decodeUnknownSync(FileFromSelf)
console.log(decode(new File([], ""))) // File { size: 0, type: '', name: '', lastModified: 1705595977234 }
decode(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
decode: (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(new ParseResult.Type(ast, input))
},
// Encoding function
encode: (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(new ParseResult.Type(ast, input))
}
},
{
description: `ReadonlySet<${S.format(item)}>`
}
)
// const setOfNumbers: S.Schema<ReadonlySet<string>, ReadonlySet<number>>
const setOfNumbers = MyReadonlySet(S.NumberFromString)
const decode = S.decodeUnknownSync(setOfNumbers)
console.log(decode(new Set(["1", "2", "3"]))) // Set(3) { 1, 2, 3 }
decode(null)
/*
throws
Error: Expected ReadonlySet<NumberFromString>, actual null
*/
decode(new Set(["1", null, "3"]))
/*
throws
Error: ReadonlySet<NumberFromString>
└─ ReadonlyArray<NumberFromString>
└─ [1]
└─ NumberFromString
└─ From side transformation failure
└─ Expected a string, actual null
*/
[!WARNING] The decoding and encoding functions cannot use context (the
Rtype parameter) and cannot use async effects.
When you define a new data type, some compilers like Arbitrary or Pretty may not know how to handle the newly defined data. For instance:
import { Arbitrary, Schema } from "@effect/schema"
const isFile = (input: unknown): input is File => input instanceof File
const FileFromSelf = Schema.declare(isFile, {
identifier: "FileFromSelf"
})
// Create an Arbitrary instance for FileFromSelf schema
const arb = Arbitrary.make(FileFromSelf)
/*
throws:
Error: cannot build an Arbitrary for a declaration without annotations (FileFromSelf)
*/
In such cases, you need to provide annotations to ensure proper functionality:
import { Arbitrary, FastCheck, Pretty, Schema } from "@effect/schema"
const isFile = (input: unknown): input is File => input instanceof File
const FileFromSelf = Schema.declare(isFile, {
identifier: "FileFromSelf",
// Provide an arbitrary function to generate random File instances
arbitrary: () => (fc) =>
fc
.tuple(fc.string(), fc.string())
.map(([path, content]) => new File([content], path)),
// Provide a pretty function to generate human-readable representation of File instances
pretty: () => (file) => `File(${file.name})`
})
// Create an Arbitrary instance for FileFromSelf schema
const arb = Arbitrary.make(FileFromSelf)
// Generate sample files using the Arbitrary instance
const files = FastCheck.sample(arb, 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.String.pipe(
S.filter((value) => {
try {
new URL(value)
return true
} catch (_) {
return false
}
})
)
const decode = S.decodeUnknownSync(UrlString)
console.log(decode("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.String.pipe(
S.filter((value) => {
try {
return new URL(value).toString() === value
} catch (_) {
return false
}
})
)
const NormalizeUrlString = S.transformOrFail(S.String, NormalizedUrlString, {
decode: (value, _, ast) =>
ParseResult.try({
try: () => new URL(value).toString(),
catch: (err) =>
new ParseResult.Type(
ast,
value,
err instanceof Error ? err.message : undefined
)
}),
encode: ParseResult.succeed
})
const decode = S.decodeUnknownSync(NormalizeUrlString)
console.log(decode("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"
const pair = <A, I, R>(
schema: S.Schema<A, I, R>
): S.Schema<readonly [A, A], readonly [I, I], R> => {
const element = new AST.Element(
schema.ast, // <= the element type
false // <= is optional?
)
const tuple = new AST.TupleType(
[element, element], // <= elements definitions
[], // <= 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.
You can manage these annotations using the annotations method.
Let's see some examples:
import * as S from "@effect/schema/Schema"
const Password =
// initial schema, a string
S.String
// add an error message for non-string values
.annotations({ message: () => "not a string" })
.pipe(
// add a constraint to the schema, only non-empty strings are valid
// and add an error message for empty strings
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
S.maxLength(10, { message: (s) => `${s} is too long` })
// add an identifier to the schema
)
.annotations({
// add an identifier to the schema
identifier: "Password",
// add a title to the schema
title: "password",
// add a description to the schema
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
examples: ["1Ki77y", "jelly22fi$h"],
// add documentation to the schema
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 annotation.
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.annotations(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.annotations(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.annotations({ identifier: "Name" }),
age: S.Number.annotations({ identifier: "Age" })
}).annotations({ 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.annotations({ identifier: "Name" }), // refinement
age: S.Positive.pipe(S.int({ identifier: "Age" })) // refinement
}).annotations({ 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.
The general logic followed to determine the messages is as follows:
In practice, either only default messages are used or only custom messages are used. This is to address the scenario where a user wants to define a single cumulative custom message describing the properties that a valid value must have and does not want to see default messages. Therefore, in the presence of even a single custom message, if different messages are desired for even the innermost schemas, custom messages must also be set for those.
You have the option to customize error messages for refinements using the message annotation:
import * as S from "@effect/schema/Schema"
const schema = S.Struct({
name: S.NonEmpty.annotations({
identifier: "Name",
message: () => "Name: a required non empty string"
}),
age: S.Positive.pipe(
S.int({ identifier: "Age", message: () => "Age: a positive integer" })
)
}).annotations({ 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.annotations({ message: () => "please enter a number" }).pipe(
S.positive({ message: () => "please enter a positive number" }),
S.int({ message: () => "please enter an integer" })
)
}).annotations({ 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, {
decode: (s, _, ast) => {
const n = Number(s)
return Number.isNaN(n)
? ParseResult.fail(new ParseResult.Type(ast, s))
: ParseResult.succeed(n)
},
encode: (n) => ParseResult.succeed(String(n))
}).annotations({ identifier: "IntFromString" })
const schema = S.Struct({
name: S.NonEmpty,
age: IntFromString
}).annotations({ identifier: "Person" })
// "from" failure
S.decodeUnknownSync(schema)({ name: "John", age: null })
/*
throws:
Error: Person
└─ ["age"]
└─ IntFromString
└─ Encoded side transformation failure
└─ Expected a string, actual null
*/
// "to" failure
S.decodeUnknownSync(schema)({ name: "John", age: "1.2" })
/*
throws:
Error: Person
└─ ["age"]
└─ IntFromString
└─ Type 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 message annotation:
import * as ParseResult from "@effect/schema/ParseResult"
import * as S from "@effect/schema/Schema"
const IntFromString = S.transformOrFail(
S.String.annotations({ message: () => "please enter a string" }),
S.Int.annotations({ message: () => "please enter an integer" }),
{
decode: (s, _, ast) => {
const n = Number(s)
return Number.isNaN(n)
? ParseResult.fail(new ParseResult.Type(ast, s))
: ParseResult.succeed(n)
},
encode: (n) => ParseResult.succeed(String(n))
}
).annotations({
identifier: "IntFromString",
message: () => "please enter a parseable string"
})
const schema = S.Struct({
name: S.NonEmpty,
age: IntFromString
}).annotations({ 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
*/
Messages are not only of type string but can return an Effect so that they can have dependencies (for example, from an internationalization service). Let's see the outline of a similar situation with a very simplified example for demonstration purposes:
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 Either from "effect/Either"
import * as Option from "effect/Option"
// internationalization service
class Messages extends Context.Tag("Messages")<
Messages,
{
NonEmpty: string
}
>() {}
const Name = S.NonEmpty.pipe(
S.message(() =>
Effect.gen(function* (_) {
const service = yield* _(Effect.serviceOption(Messages))
return Option.match(service, {
onNone: () => "Invalid string",
onSome: (messages) => messages.NonEmpty
})
})
)
)
S.decodeUnknownSync(Name)("") // => throws "Invalid string"
const result = S.decodeUnknownEither(Name)("").pipe(
Either.mapLeft((error) =>
TreeFormatter.formatErrorEffect(error).pipe(
Effect.provideService(Messages, { NonEmpty: "should be non empty" }),
Effect.runSync
)
)
)
console.log(result) // => { _id: 'Either', _tag: 'Left', left: 'should be non empty' }
Feature-wise, schema can do practically everything that zod can do.
The main differences are:
schema transformations are bidirectional, so it not only decodes like zod but also encodes.schema is integrated with Effect and inherits some benefits from it (such as dependency tracking in transformations).schema is highly customizable through annotations, allowing users to attach meta-information.schema uses a functional programming style with combinators and transformations (while zod provides a chainable API).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
The npm package @effect/schema receives a total of 713,004 weekly downloads. As such, @effect/schema popularity was classified as popular.
We found that @effect/schema demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 3 open source maintainers collaborating on the project.
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