Getting Started

You can import the necessary types and functions from the effect/Schema module:

Example (Namespace Import)

import * as Schema from "effect/Schema"

Example (Named Import)

import { Schema } from "effect"

Defining a schema

One common way to define a Schema is by utilizing the Struct constructor. This constructor 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.

Example

Consider the following schema that describes a person object with:

• a name property of type string
• an age property of type number

import { Schema } from "effect"

const Person = Schema.Struct({
  name: Schema.String,
  age: Schema.Number
})

Extracting Inferred Types

Type

Once you’ve defined a schema (Schema<Type, Encoded, Context>), you can extract the inferred type Type in two ways:

1. Using the Schema.Type utility.
2. Using the Type field defined on your schema.

Example

import { Schema } from "effect"

const Person = Schema.Struct({
  name: Schema.String,
  age: Schema.Number
})

// 1. Using the Schema.Type utility
type Person = Schema.Schema.Type<typeof Person>

// 2. Using the `Type` field
type Person2 = typeof Person.Type

The result is equivalent to:

type Person = {
  name: string
  age: number
}

Alternatively, you can extract the Person type using the interface keyword.

Example

import { Schema } from "effect"

const Person = Schema.Struct({
  name: Schema.String,
  age: Schema.Number
})

interface Person extends Schema.Schema.Type<typeof Person> {}

Both approaches yield the same result, but using an interface provides benefits such as performance advantages and improved readability.

Encoded

In cases where in a Schema<Type, Encoded, Context> the Encoded type differs from the Type type, you can extract the inferred Encoded in two ways:

1. Using the Schema.Encoded utility.
2. Using the Encoded field defined on your schema.

Example

import { Schema } from "effect"

const Person = Schema.Struct({
  name: Schema.String,
  // a schema that decodes a string to a number
  age: Schema.NumberFromString
})

// 1. Using the Schema.Encoded utility
type PersonEncoded = Schema.Schema.Encoded<typeof Person>

// 2. Using the `Encoded` field
type PersonEncoded2 = typeof Person.Encoded

The result is equivalent to:

type PersonEncoded = {
  name: string
  age: string
}

Note that age is of type string in the Encoded type of the schema and is of type number in the Type type of the schema.

Alternatively, you can extract the PersonEncoded type using the interface keyword.

Example

import { Schema } from "effect"

const Person = Schema.Struct({
  name: Schema.String,
  // a schema that decodes a string to a number
  age: Schema.NumberFromString
})

interface PersonEncoded extends Schema.Schema.Encoded<typeof Person> {}

Both approaches yield the same result, but using an interface provides benefits such as performance advantages and improved readability.

Context

You can also extract the inferred type Context that represents the context required by the schema.

Example

import { Schema } from "effect"

const Person = Schema.Struct({
  name: Schema.String,
  age: Schema.Number
})

// 1. Using the Schema.Context utility
type PersonContext = Schema.Schema.Context<typeof Person>

// 2. Using the `Context` field
type PersonContext2 = typeof Person.Context

Schemas with Opaque Types

When defining a schema, you may want to create a schema with an opaque type. This is useful when you want to hide the internal structure of the schema and only expose the type of the schema.

Example

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

import { Schema } from "effect"

const _Person = Schema.Struct({
  name: Schema.String,
  age: Schema.Number
})

interface Person extends Schema.Schema.Type<typeof _Person> {}

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

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

Note that the technique shown above becomes more complex when the schema is defined such that Type is different from Encoded.

Example

import { Schema } from "effect"

const _Person = Schema.Struct({
  name: Schema.String,
  // a schema that decodes a string to a number
  age: Schema.NumberFromString
})

interface Person extends Schema.Schema.Type<typeof _Person> {}

interface PersonEncoded extends Schema.Schema.Encoded<typeof _Person> {}

// Re-declare the schema to create a schema with an opaque type
const Person: Schema.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.

Readonly Types by Default

It’s important to note that by default, most constructors exported by effect/Schema return readonly types.

Example

For instance, in the Person schema below:

import { Schema } from "effect"

const Person = Schema.Struct({
  name: Schema.String,
  age: Schema.Number
})

the resulting inferred Type would be:

{
  readonly name: string;
  readonly age: number;
}

Decoding

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.

API	Description
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.

decodeUnknownSync

The Schema.decodeUnknownSync function is useful when you want to parse a value and immediately throw an error if the parsing fails.

Example

import { Schema } from "effect"

const Person = Schema.Struct({
  name: Schema.String,
  age: Schema.Number
})

// Simulate an unknown input
const input: unknown = { name: "Alice", age: 30 }

console.log(Schema.decodeUnknownSync(Person)(input))
// Output: { name: 'Alice', age: 30 }

console.log(Schema.decodeUnknownSync(Person)(null))
/*
throws:
ParseError: Expected { readonly name: string; readonly age: number }, actual null
*/

decodeUnknownEither

The Schema.decodeUnknownEither function returns an Either type representing success or failure.

Example

import { Schema } from "effect"
import { Either } from "effect"

const Person = Schema.Struct({
  name: Schema.String,
  age: Schema.Number
})

const decode = Schema.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 { readonly name: string; readonly age: number }, actual null'
  }
  */
}

decodeUnknown

If your schema involves asynchronous transformations, the Schema.decodeUnknownSync and Schema.decodeUnknownEither functions will not be suitable. In such cases, you should use the Schema.decodeUnknown function, which returns an Effect.

Example

import { Schema } from "effect"
import { Effect } from "effect"

const PersonId = Schema.Number

const Person = Schema.Struct({
  id: PersonId,
  name: Schema.String,
  age: Schema.Number
})

const asyncSchema = Schema.transformOrFail(PersonId, Person, {
  strict: true,
  // 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"))
})

console.log(Schema.decodeUnknownEither(asyncSchema)(1))
/*
Output:
{
  _id: 'Either',
  _tag: 'Left',
  left: {
    _id: 'ParseError',
    message: '(number <-> { readonly id: number; readonly name: string; readonly age: number })\n' +
      '└─ cannot be be resolved synchronously, this is caused by using runSync on an effect that performs async work'
  }
}
*/

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

In the code above, the first approach using Schema.decodeUnknownEither results in an error indicating that the transformation cannot be resolved synchronously. This occurs because Schema.decodeUnknownEither is not designed for async operations. The second approach, which uses Schema.decodeUnknown, works correctly, allowing you to handle asynchronous transformations and return the expected result.

Encoding

The Schema module provides several encode* functions to encode data according to a schema:

API	Description
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.

Example (Schema.encodeSync)

import { Schema } from "effect"

const Person = Schema.Struct({
  // A schema that ensures a string is not empty
  name: Schema.NonEmptyString,
  // A schema that can decode a string to a number
  // and encode a number to a string
  age: Schema.NumberFromString
})

// Valid input
console.log(Schema.encodeSync(Person)({ name: "Alice", age: 30 }))
// Output: { name: 'Alice', age: '30' }

// Invalid input
console.log(Schema.encodeSync(Person)({ name: "", age: 30 }))
/*
throws:
ParseError: { readonly name: NonEmptyString; readonly age: NumberFromString }
└─ ["name"]
   └─ NonEmptyString
      └─ Predicate refinement failure
         └─ Expected NonEmptyString, actual ""
*/

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

Handling Unsupported Encoding

Although it is generally recommended to define schemas that support both decoding and encoding, there are situations where encoding support might be impossible. In such cases, the Forbidden error can be used to handle unsupported encoding.

Example (Using Forbidden for Unsupported Encoding)

Here is an example of a transformation that never fails during decoding. It returns an Either containing either the decoded value or the original input. For encoding, it is reasonable to not support it and use Forbidden as the result.

import { Either, ParseResult, Schema } from "effect"

// Define a schema that safely decodes to Either type
export const SafeDecode = <A, I>(self: Schema.Schema<A, I, never>) => {
  const decodeUnknownEither = Schema.decodeUnknownEither(self)
  return Schema.transformOrFail(
    Schema.Unknown,
    Schema.EitherFromSelf({
      left: Schema.Unknown,
      right: Schema.typeSchema(self)
    }),
    {
      strict: true,
      decode: (input) =>
        ParseResult.succeed(
          Either.mapLeft(decodeUnknownEither(input), () => input)
        ),
      encode: (actual, _, ast) =>
        Either.match(actual, {
          // Encoding a Left value is not supported
          onLeft: () =>
            ParseResult.fail(
              new ParseResult.Forbidden(
                ast,
                actual,
                "cannot encode a Left"
              )
            ),
          // Successfully encode a Right value
          onRight: ParseResult.succeed
        })
    }
  )
}

Explanation

• Decoding: The SafeDecode function ensures that decoding never fails. It wraps the decoded value in an Either, where a successful decoding results in a Right and a failed decoding results in a Left containing the original input.
• Encoding: The encoding process uses the Forbidden error to indicate that encoding a Left value is not supported. Only Right values are successfully encoded.

ParseError

The Schema.decodeUnknownEither and Schema.encodeEither functions returns a Either:

Either<Type, ParseError>

where ParseError is defined as follows (simplified):

interface ParseError {
  readonly _tag: "ParseError"
  readonly issue: ParseIssue
}

In this structure, 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<Type, ParseError> contains the inferred data type described by the schema (Type). A successful parse yields a Right value with the parsed data Type, while a failed parse results in a Left value containing a ParseError.

Tip

By default only the first error is returned. You can use the errors option to receive all errors.

Parse Options

The following options affect both decoding and encoding.

Managing Excess properties

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.

Example (onExcessProperty set to "error")

import { Schema } from "effect"

const Person = Schema.Struct({
  name: Schema.String,
  age: Schema.Number
})

console.log(
  Schema.decodeUnknownSync(Person)({
    name: "Bob",
    age: 40,
    email: "[email protected]"
  })
)
/*
Output:
{ name: 'Bob', age: 40 }
*/

Schema.decodeUnknownSync(Person)(
  {
    name: "Bob",
    age: 40,
    email: "[email protected]"
  },
  { onExcessProperty: "error" }
)
/*
throws
ParseError: { readonly name: string; readonly age: number }
└─ ["email"]
   └─ is unexpected, expected: "name" | "age"
*/

If you want to allow excess properties to remain, you can use onExcessProperty set to "preserve".

Example (onExcessProperty set to "preserve")

import { Schema } from "effect"

const Person = Schema.Struct({
  name: Schema.String,
  age: Schema.Number
})

console.log(
  Schema.decodeUnknownSync(Person)(
    {
      name: "Bob",
      age: 40,
      email: "[email protected]"
    },
    { onExcessProperty: "preserve" }
  )
)
/*
{ email: '[email protected]', name: 'Bob', age: 40 }
*/

Receive all errors

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

Example (errors set to "all")

import { Schema } from "effect"

const Person = Schema.Struct({
  name: Schema.String,
  age: Schema.Number
})

Schema.decodeUnknownSync(Person)(
  {
    name: "Bob",
    age: "abc",
    email: "[email protected]"
  },
  { errors: "all", onExcessProperty: "error" }
)
/*
throws
ParseError: { readonly name: string; readonly age: number }
├─ ["email"]
│  └─ is unexpected, expected: "name" | "age"
└─ ["age"]
   └─ Expected number, actual "abc"
*/

Managing Property Order

The propertyOrder option provides control over the order of object fields in the output. This feature is particularly useful when the sequence of keys is important for the consuming processes or when maintaining the input order enhances readability and usability.

By default, the propertyOrder option is set to "none". This means that the internal system decides the order of keys to optimize parsing speed. The order of keys in this mode should not be considered stable, and it’s recommended not to rely on key ordering as it may change in future updates.

Setting propertyOrder to "original" ensures that the keys are ordered as they appear in the input during the decoding/encoding process.

Example (Synchronous Decoding)

import { Schema } from "effect"

const schema = Schema.Struct({
  a: Schema.Number,
  b: Schema.Literal("b"),
  c: Schema.Number
})

// Decoding an object synchronously without specifying the property order
console.log(Schema.decodeUnknownSync(schema)({ b: "b", c: 2, a: 1 }))
// Output decided internally: { a: 1, b: 'b', c: 2 }

// Decoding an object synchronously while preserving the order of properties as in the input
console.log(
  Schema.decodeUnknownSync(schema)(
    { b: "b", c: 2, a: 1 },
    { propertyOrder: "original" }
  )
)
// Output preserving input order: { b: 'b', c: 2, a: 1 }

Example (Asynchronous Decoding)

import type { Duration } from "effect"
import { Effect, ParseResult, Schema } from "effect"

// Function to simulate an asynchronous process within the schema
const effectify = (duration: Duration.DurationInput) =>
  Schema.Number.pipe(
    Schema.transformOrFail(Schema.Number, {
      strict: true,
      decode: (x) =>
        Effect.sleep(duration).pipe(
          Effect.andThen(ParseResult.succeed(x))
        ),
      encode: ParseResult.succeed
    })
  )

// Define a structure with asynchronous behavior in each field
const schema = Schema.Struct({
  a: effectify("200 millis"),
  b: effectify("300 millis"),
  c: effectify("100 millis")
}).annotations({ concurrency: 3 })

// Decoding data asynchronously without preserving order
Schema.decode(schema)({ a: 1, b: 2, c: 3 })
  .pipe(Effect.runPromise)
  .then(console.log)
// Output decided internally: { c: 3, a: 1, b: 2 }

// Decoding data asynchronously while preserving the original input order
Schema.decode(schema)({ a: 1, b: 2, c: 3 }, { propertyOrder: "original" })
  .pipe(Effect.runPromise)
  .then(console.log)
// Output preserving input order: { a: 1, b: 2, c: 3 }

Customizing Parsing Behavior at the Schema Level

You can tailor parse options for each schema using the parseOptions annotation. These options allow for specific parsing behavior at various levels of the schema hierarchy, overriding any parent settings and cascading down to nested schemas.

Example

import { Schema } from "effect"
import { Either } from "effect"

const schema = Schema.Struct({
  a: Schema.Struct({
    b: Schema.String,
    c: Schema.String
  }).annotations({
    title: "first error only",
    // Only the first error in this sub-schema is reported
    parseOptions: { errors: "first" }
  }),
  d: Schema.String
}).annotations({
  title: "all errors",
  // All errors in the main schema are reported
  parseOptions: { errors: "all" }
})

const result = Schema.decodeUnknownEither(schema)(
  { a: {} },
  { errors: "first" }
)
if (Either.isLeft(result)) {
  console.log(result.left.message)
}
/*
all errors
├─ ["a"]
│  └─ first error only
│     └─ ["b"]
│        └─ is missing
└─ ["d"]
   └─ is missing
*/

Detailed Output Explanation:

In this example:

• The main schema is configured to display all errors. Hence, you will see errors related to both the d field (since it’s missing) and any errors from the a subschema.
• The subschema (a) is set to display only the first error. Although both b and c fields are missing, only the first missing field (b) is reported.

Type Guards

The Schema.is function provides a way to verify if a value conforms to a given schema. It acts as a type guard, taking a value of type unknown and determining if it matches the structure and type constraints defined in the schema.

Here’s how the Schema.is function works:

1. Schema Definition: Define a schema to describe the structure and constraints of the data type you expect. For instance, Schema<Type, Encoded, Context>, where Type is the target type you want to validate against.

2. Type Guard Creation: Use the schema to create a user-defined type guard, (u: unknown) => u is Type. This function can be used at runtime to check if a value meets the requirements of the schema.

Role of the Encoded Type in Type Guards

The type Encoded, which is often used in schema transformations, does not affect the creation of the type guard. The main purpose is to ensure that the input matches the desired type Type.

Example (Creating and Using a Type Guard)

import { Schema } from "effect"

// Define a schema for a Person object
const Person = Schema.Struct({
  name: Schema.String,
  age: Schema.Number
})

// Create a type guard using the schema
const isPerson = Schema.is(Person)

// Test the type guard with different values
console.log(isPerson({ name: "Alice", age: 30 })) // true
console.log(isPerson(null)) // false
console.log(isPerson({})) // false

The isPerson function generated from the schema has the following signature:

const isPerson: (
  u: unknown,
  overrideOptions?: number | ParseOptions
) => u is {
  readonly name: string
  readonly age: number
}

Assertions

While type guards verify whether a value conforms to a specific type, the Schema.asserts function goes further by asserting that an input matches the schema type Type (from Schema<Type, Encoded, Context>). If the input does not match the schema, it throws a detailed error, making it useful for runtime validation.

Role of the Encoded Type in Assertions

The type Encoded, which is often used in schema transformations, does not affect the creation of the assertion. The main purpose is to ensure that the input matches the desired type Type.

Example (Creating and Using an Assertion)

import { Schema } from "effect"

// Define a schema for a Person object
const Person = Schema.Struct({
  name: Schema.String,
  age: Schema.Number
})

// Create an assertion function from the schema
const assertsPerson: Schema.Schema.ToAsserts<typeof Person> =
  Schema.asserts(Person)

try {
  // Attempt to assert that the input matches the Person schema
  assertsPerson({ name: "Alice", age: "30" })
} catch (e) {
  console.error("The input does not match the schema:")
  console.error(e)
}
/*
throws:
The input does not match the schema:
{
  _id: 'ParseError',
  message: '{ readonly name: string; readonly age: number }\n' +
    '└─ ["age"]\n' +
    '   └─ Expected number, actual "30"'
}
*/

// This input matches the schema and will not throw an error
assertsPerson({ name: "Alice", age: 30 })

The assertsPerson function generated from the schema has the following signature:

const assertsPerson: (
  input: unknown,
  overrideOptions?: number | ParseOptions
) => asserts input is {
  readonly name: string
  readonly age: number
}

Managing Missing Properties

When decoding, it’s important to understand how missing properties are processed. By default, if a property is not present in the input, it is treated as if it were present with an undefined value.

Example (Default Behavior)

import { Schema } from "effect"

const schema = Schema.Struct({ a: Schema.Unknown })
const input = {}

console.log(Schema.decodeUnknownSync(schema)(input))
// Output: { a: undefined }

In this example, although the key "a" is not present in the input, it is treated as { a: undefined } by default.

If your validation logic needs to distinguish between truly missing properties and those that are explicitly undefined, you can enable the exact option:

Example (exact set to true)

import { Schema } from "effect"

const schema = Schema.Struct({ a: Schema.Unknown })
const input = {}

console.log(Schema.decodeUnknownSync(schema)(input, { exact: true }))
/*
throws
ParseError: { readonly a: unknown }
└─ ["a"]
   └─ is missing
*/

For the APIs Schema.is and Schema.asserts, however, the default behavior is to treat missing properties strictly, where the default for exact is true:

Example (Default Behavior for Schema.is and Schema.asserts)

import type { SchemaAST } from "effect"
import { Schema } from "effect"

const schema = Schema.Struct({ a: Schema.Unknown })
const input = {}

console.log(Schema.is(schema)(input)) // Output: false
console.log(Schema.is(schema)(input, { exact: false })) // Output: true

const asserts: (
  u: unknown,
  overrideOptions?: SchemaAST.ParseOptions
) => asserts u is {
  readonly a: unknown
} = Schema.asserts(schema)

try {
  asserts(input)
  console.log("asserts passed")
} catch (e: any) {
  console.error("asserts failed")
  console.error(e.message)
}
/*
Output:
asserts failed
{ readonly a: unknown }
└─ ["a"]
  └─ is missing
*/

try {
  asserts(input, { exact: false })
  console.log("asserts passed")
} catch (e: any) {
  console.error("asserts failed")
  console.error(e.message)
}
// Output: asserts passed

Naming Conventions

The naming conventions in effect/Schema are designed to be straightforward and logical, focusing primarily on compatibility with JSON serialization. This approach simplifies the understanding and use of schemas, especially for developers who are integrating web technologies where JSON is a standard data interchange format.

Overview of Naming Strategies

JSON-Compatible Types

Schemas that naturally serialize to JSON-compatible formats are named directly after their data types.

For instance:

• Schema.Date: serializes JavaScript Date objects to ISO-formatted strings, a typical method for representing dates in JSON.
• Schema.Number: used directly as it maps precisely to the JSON number type, requiring no special transformation to remain JSON-compatible.

Non-JSON-Compatible Types

When dealing with types that do not have a direct representation in JSON, the naming strategy incorporates additional details to indicate the necessary transformation. This helps in setting clear expectations about the schema’s behavior:

For instance:

• Schema.DateFromSelf: indicates that the schema handles Date objects, which are not natively JSON-serializable.
• Schema.NumberFromString: this naming suggests that the schema processes numbers that are initially represented as strings, emphasizing the transformation from string to number when decoding.

The primary goal of these schemas is to ensure that domain objects can be easily serialized (“encoded”) and deserialized (“decoded”) for transmission over network connections, thus facilitating their transfer between different parts of an application or across different applications.

Rationale

While JSON’s ubiquity justifies its primary consideration in naming, the conventions also accommodate serialization for other types of transport. For instance, converting a Date to a string is a universally useful method for various communication protocols, not just JSON. Thus, the selected naming conventions serve as sensible defaults that prioritize clarity and ease of use, facilitating the serialization and deserialization processes across diverse technological environments.