majiayu000 Gleam Type System
Introduction
Gleam is a statically-typed functional language that compiles to Erlang and JavaScript, bringing modern type safety to the BEAM ecosystem. Its type system prevents entire categories of runtime errors while maintaining the concurrency and fault-tolerance benefits of the Erlang VM.
The type system features algebraic data types, parametric polymorphism, type inference, exhaustive pattern matching, and no null values. Every value is typed, and the compiler enforces type safety at compile time, eliminating common bugs before code runs.
This skill covers custom types and ADTs, pattern matching, generic types, Result and Option types, type aliases, opaque types, type inference, and patterns for type-safe error handling on the BEAM.
Custom Types and Records
Custom types define structured data with named fields, providing type-safe access and pattern matching.
// Simple custom type (record)
pub type User {
User(name: String, age: Int, email: String)
}
// Creating instances
pub fn create_user() -> User {
User(name: "Alice", age: 30, email: "alice@example.com")
}
// Accessing fields
pub fn get_user_name(user: User) -> String {
user.name
}
pub fn get_user_age(user: User) -> Int {
user.age
}
// Updating records (immutable)
pub fn birthday(user: User) -> User {
User(..user, age: user.age + 1)
}
pub fn change_email(user: User, new_email: String) -> User {
User(..user, email: new_email)
}
// Multiple constructors
pub type Shape {
Circle(radius: Float)
Rectangle(width: Float, height: Float)
Triangle(base: Float, height: Float)
}
pub fn area(shape: Shape) -> Float {
case shape {
Circle(radius) -> 3.14159 *. radius *. radius
Rectangle(width, height) -> width *. height
Triangle(base, height) -> base *. height /. 2.0
}
}
// Tuple structs (unlabeled fields)
pub type Point {
Point(Float, Float)
}
pub fn distance(p1: Point, p2: Point) -> Float {
let Point(x1, y1) = p1
let Point(x2, y2) = p2
let dx = x2 -. x1
let dy = y2 -. y1
float.square_root(dx *. dx +. dy *. dy)
}
// Nested custom types
pub type Address {
Address(street: String, city: String, zip: String)
}
pub type Person {
Person(name: String, age: Int, address: Address)
}
pub fn get_city(person: Person) -> String {
person.address.city
}
// Generic custom types
pub type Box(a) {
Box(value: a)
}
pub fn box_map(box: Box(a), f: fn(a) -> b) -> Box(b) {
Box(value: f(box.value))
}
pub fn unbox(box: Box(a)) -> a {
box.value
}
// Recursive types
pub type Tree(a) {
Leaf(value: a)
Branch(left: Tree(a), right: Tree(a))
}
pub fn tree_depth(tree: Tree(a)) -> Int {
case tree {
Leaf(_) -> 1
Branch(left, right) -> 1 + int.max(tree_depth(left), tree_depth(right))
}
}
// Phantom types for type-safe APIs
pub type Validated
pub type Unvalidated
pub type Email(state) {
Email(value: String)
}
pub fn create_email(value: String) -> Email(Unvalidated) {
Email(value: value)
}
pub fn validate_email(email: Email(Unvalidated)) ->
Result(Email(Validated), String) {
case string.contains(email.value, "@") {
True -> Ok(Email(value: email.value))
False -> Error("Invalid email format")
}
}
pub fn send_email(email: Email(Validated)) -> Nil {
// Only validated emails can be sent
io.println("Sending email to: " <> email.value)
}
Custom types provide named, type-safe data structures with exhaustive pattern matching guarantees.
Algebraic Data Types
ADTs model data with multiple variants, enabling exhaustive pattern matching and making invalid states unrepresentable.
// Sum type (enum)
pub type Status {
Pending
Approved
Rejected
}
pub fn status_to_string(status: Status) -> String {
case status {
Pending -> "Pending"
Approved -> "Approved"
Rejected -> "Rejected"
}
}
// Result type (built-in ADT)
pub type Result(ok, error) {
Ok(ok)
Error(error)
}
pub fn parse_int(str: String) -> Result(Int, String) {
case int.parse(str) {
Ok(n) -> Ok(n)
Error(_) -> Error("Not a valid integer")
}
}
pub fn handle_result(result: Result(Int, String)) -> String {
case result {
Ok(n) -> "Got number: " <> int.to_string(n)
Error(msg) -> "Error: " <> msg
}
}
// Option type pattern
pub type Option(a) {
Some(a)
None
}
pub fn find_user(id: Int) -> Option(User) {
case id {
1 -> Some(User(name: "Alice", age: 30, email: "alice@example.com"))
_ -> None
}
}
pub fn option_map(opt: Option(a), f: fn(a) -> b) -> Option(b) {
case opt {
Some(value) -> Some(f(value))
None -> None
}
}
pub fn option_unwrap_or(opt: Option(a), default: a) -> a {
case opt {
Some(value) -> value
None -> default
}
}
// Complex ADTs
pub type HttpResponse {
Ok200(body: String)
Created201(body: String, location: String)
BadRequest400(message: String)
NotFound404
ServerError500(message: String)
}
pub fn handle_response(response: HttpResponse) -> String {
case response {
Ok200(body) -> "Success: " <> body
Created201(body, location) -> "Created at " <> location <> ": " <> body
BadRequest400(message) -> "Bad request: " <> message
NotFound404 -> "Resource not found"
ServerError500(message) -> "Server error: " <> message
}
}
// Linked list ADT
pub type List(a) {
Nil
Cons(head: a, tail: List(a))
}
pub fn list_length(list: List(a)) -> Int {
case list {
Nil -> 0
Cons(_, tail) -> 1 + list_length(tail)
}
}
pub fn list_map(list: List(a), f: fn(a) -> b) -> List(b) {
case list {
Nil -> Nil
Cons(head, tail) -> Cons(f(head), list_map(tail, f))
}
}
// Either type
pub type Either(left, right) {
Left(left)
Right(right)
}
pub fn partition_either(list: List(Either(a, b))) -> #(List(a), List(b)) {
case list {
Nil -> #(Nil, Nil)
Cons(Left(a), tail) -> {
let #(lefts, rights) = partition_either(tail)
#(Cons(a, lefts), rights)
}
Cons(Right(b), tail) -> {
let #(lefts, rights) = partition_either(tail)
#(lefts, Cons(b, rights))
}
}
}
// State machine with ADTs
pub type ConnectionState {
Disconnected
Connecting(attempt: Int)
Connected(session_id: String)
Disconnecting
}
pub fn handle_connect_event(state: ConnectionState) -> ConnectionState {
case state {
Disconnected -> Connecting(attempt: 1)
Connecting(attempt) if attempt < 3 -> Connecting(attempt: attempt + 1)
Connecting(_) -> Disconnected
Connected(_) -> state
Disconnecting -> state
}
}
// Expression tree ADT
pub type Expr {
Number(Float)
Add(left: Expr, right: Expr)
Subtract(left: Expr, right: Expr)
Multiply(left: Expr, right: Expr)
Divide(left: Expr, right: Expr)
}
pub fn evaluate(expr: Expr) -> Result(Float, String) {
case expr {
Number(n) -> Ok(n)
Add(left, right) -> {
use l <- result.try(evaluate(left))
use r <- result.try(evaluate(right))
Ok(l +. r)
}
Subtract(left, right) -> {
use l <- result.try(evaluate(left))
use r <- result.try(evaluate(right))
Ok(l -. r)
}
Multiply(left, right) -> {
use l <- result.try(evaluate(left))
use r <- result.try(evaluate(right))
Ok(l *. r)
}
Divide(left, right) -> {
use l <- result.try(evaluate(left))
use r <- result.try(evaluate(right))
case r {
0.0 -> Error("Division by zero")
_ -> Ok(l /. r)
}
}
}
}
ADTs enable type-safe modeling of complex domain logic with compiler-verified exhaustiveness.
Pattern Matching
Pattern matching provides exhaustive, type-safe conditional logic with destructuring capabilities.
// Basic pattern matching
pub fn describe_number(n: Int) -> String {
case n {
0 -> "zero"
1 -> "one"
2 -> "two"
_ -> "many"
}
}
// Pattern matching with guards
pub fn classify_age(age: Int) -> String {
case age {
n if n < 0 -> "Invalid"
n if n < 13 -> "Child"
n if n < 20 -> "Teen"
n if n < 65 -> "Adult"
_ -> "Senior"
}
}
// Destructuring tuples
pub fn swap(pair: #(a, b)) -> #(b, a) {
let #(first, second) = pair
#(second, first)
}
pub fn tuple_pattern(tuple: #(Int, String, Bool)) -> String {
case tuple {
#(0, _, _) -> "First is zero"
#(_, "hello", _) -> "Second is hello"
#(_, _, True) -> "Third is true"
_ -> "Something else"
}
}
// Destructuring custom types
pub fn greet_user(user: User) -> String {
let User(name: name, age: age, email: _) = user
"Hello " <> name <> ", you are " <> int.to_string(age)
}
pub fn is_circle(shape: Shape) -> Bool {
case shape {
Circle(_) -> True
_ -> False
}
}
// Nested pattern matching
pub type Nested {
Outer(inner: Inner)
}
pub type Inner {
Value(Int)
Empty
}
pub fn extract_value(nested: Nested) -> Option(Int) {
case nested {
Outer(Value(n)) -> Some(n)
Outer(Empty) -> None
}
}
// List pattern matching
pub fn list_sum(list: List(Int)) -> Int {
case list {
[] -> 0
[head] -> head
[first, second] -> first + second
[head, ..tail] -> head + list_sum(tail)
}
}
pub fn list_head(list: List(a)) -> Option(a) {
case list {
[] -> None
[head, ..] -> Some(head)
}
}
// Multiple case expressions
pub fn compare_results(r1: Result(Int, String),
r2: Result(Int, String)) -> String {
case r1, r2 {
Ok(n1), Ok(n2) -> "Both ok: " <> int.to_string(n1 + n2)
Ok(n), Error(_) -> "First ok: " <> int.to_string(n)
Error(_), Ok(n) -> "Second ok: " <> int.to_string(n)
Error(e1), Error(e2) -> "Both failed: " <> e1 <> ", " <> e2
}
}
// Pattern matching with alternative patterns
pub fn is_weekend(day: String) -> Bool {
case day {
"Saturday" | "Sunday" -> True
_ -> False
}
}
// Matching on string patterns
pub fn parse_command(input: String) -> String {
case string.lowercase(input) {
"quit" | "exit" | "q" -> "Exiting..."
"help" | "h" | "?" -> "Help message"
_ -> "Unknown command"
}
}
// Use expressions for result handling
pub fn divide_and_double(a: Int, b: Int) -> Result(Int, String) {
use quotient <- result.try(case b {
0 -> Error("Division by zero")
_ -> Ok(a / b)
})
Ok(quotient * 2)
}
// Exhaustive matching on enums
pub fn status_code(status: Status) -> Int {
case status {
Pending -> 0
Approved -> 1
Rejected -> 2
}
}
Pattern matching enables concise, exhaustive conditional logic with compile-time verification.
Generic Types and Polymorphism
Generic types enable writing reusable code that works with multiple types while maintaining type safety.
// Generic function
pub fn identity(value: a) -> a {
value
}
pub fn const(a: a, b: b) -> a {
a
}
// Generic data structure
pub type Pair(a, b) {
Pair(first: a, second: b)
}
pub fn pair_map_first(pair: Pair(a, b), f: fn(a) -> c) -> Pair(c, b) {
Pair(first: f(pair.first), second: pair.second)
}
pub fn pair_map_second(pair: Pair(a, b), f: fn(b) -> c) -> Pair(a, c) {
Pair(first: pair.first, second: f(pair.second))
}
pub fn pair_swap(pair: Pair(a, b)) -> Pair(b, a) {
Pair(first: pair.second, second: pair.first)
}
// Generic container
pub type Container(a) {
Empty
Full(value: a)
}
pub fn container_map(cont: Container(a), f: fn(a) -> b) -> Container(b) {
case cont {
Empty -> Empty
Full(value) -> Full(f(value))
}
}
pub fn container_unwrap_or(cont: Container(a), default: a) -> a {
case cont {
Empty -> default
Full(value) -> value
}
}
// Higher-order functions
pub fn map(list: List(a), f: fn(a) -> b) -> List(b) {
case list {
[] -> []
[head, ..tail] -> [f(head), ..map(tail, f)]
}
}
pub fn filter(list: List(a), predicate: fn(a) -> Bool) -> List(a) {
case list {
[] -> []
[head, ..tail] -> case predicate(head) {
True -> [head, ..filter(tail, predicate)]
False -> filter(tail, predicate)
}
}
}
pub fn fold(list: List(a), initial: b, f: fn(b, a) -> b) -> b {
case list {
[] -> initial
[head, ..tail] -> fold(tail, f(initial, head), f)
}
}
// Generic Result operations
pub fn result_map(result: Result(a, e), f: fn(a) -> b) -> Result(b, e) {
case result {
Ok(value) -> Ok(f(value))
Error(err) -> Error(err)
}
}
pub fn result_map_error(result: Result(a, e), f: fn(e) -> f) -> Result(a, f) {
case result {
Ok(value) -> Ok(value)
Error(err) -> Error(f(err))
}
}
pub fn result_and_then(
result: Result(a, e),
f: fn(a) -> Result(b, e),
) -> Result(b, e) {
case result {
Ok(value) -> f(value)
Error(err) -> Error(err)
}
}
pub fn result_unwrap_or(result: Result(a, e), default: a) -> a {
case result {
Ok(value) -> value
Error(_) -> default
}
}
// Combining Results
pub fn result_all(results: List(Result(a, e))) -> Result(List(a), e) {
case results {
[] -> Ok([])
[Ok(value), ..rest] -> {
use tail <- result_and_then(result_all(rest))
Ok([value, ..tail])
}
[Error(err), ..] -> Error(err)
}
}
// Generic tree operations
pub fn tree_map(tree: Tree(a), f: fn(a) -> b) -> Tree(b) {
case tree {
Leaf(value) -> Leaf(f(value))
Branch(left, right) -> Branch(tree_map(left, f), tree_map(right, f))
}
}
pub fn tree_fold(tree: Tree(a), initial: b, f: fn(b, a) -> b) -> b {
case tree {
Leaf(value) -> f(initial, value)
Branch(left, right) -> {
let left_result = tree_fold(left, initial, f)
tree_fold(right, left_result, f)
}
}
}
// Functor pattern
pub fn functor_compose(
fa: Container(a),
f: fn(a) -> b,
g: fn(b) -> c,
) -> Container(c) {
container_map(container_map(fa, f), g)
}
Generic types enable writing reusable, type-safe abstractions that work across different concrete types.
Type Aliases and Opaque Types
Type aliases create readable names for complex types, while opaque types hide implementation details.
// Type aliases
pub type UserId = Int
pub type Email = String
pub type Age = Int
pub type UserData = #(UserId, String, Email, Age)
pub fn create_user_data(id: UserId, name: String, email: Email, age: Age) ->
UserData {
#(id, name, email, age)
}
// Function type aliases
pub type Validator(a) = fn(a) -> Result(a, String)
pub type Transformer(a, b) = fn(a) -> b
pub fn validate_age(age: Age) -> Result(Age, String) {
case age >= 0 && age <= 150 {
True -> Ok(age)
False -> Error("Invalid age")
}
}
// Collection type aliases
pub type StringList = List(String)
pub type IntResult = Result(Int, String)
pub type UserMap = Dict(UserId, User)
// Opaque types (hide internal representation)
pub opaque type Password {
Password(hash: String)
}
pub fn create_password(plain: String) -> Password {
// Hash password (simplified)
Password(hash: hash_string(plain))
}
pub fn verify_password(password: Password, plain: String) -> Bool {
let Password(hash: stored_hash) = password
stored_hash == hash_string(plain)
}
fn hash_string(s: String) -> String {
// Implementation hidden
s <> "_hashed"
}
// Opaque type for validated data
pub opaque type ValidatedEmail {
ValidatedEmail(value: String)
}
pub fn validate_and_create_email(value: String) ->
Result(ValidatedEmail, String) {
case string.contains(value, "@") {
True -> Ok(ValidatedEmail(value: value))
False -> Error("Invalid email format")
}
}
pub fn email_to_string(email: ValidatedEmail) -> String {
let ValidatedEmail(value: value) = email
value
}
// Opaque type for units
pub opaque type Meters {
Meters(Float)
}
pub opaque type Feet {
Feet(Float)
}
pub fn meters(value: Float) -> Meters {
Meters(value)
}
pub fn feet(value: Float) -> Feet {
Feet(value)
}
pub fn meters_to_feet(m: Meters) -> Feet {
let Meters(value) = m
Feet(value *. 3.28084)
}
pub fn feet_to_meters(f: Feet) -> Meters {
let Feet(value) = f
Meters(value /. 3.28084)
}
// Opaque type for IDs
pub opaque type OrderId {
OrderId(Int)
}
pub fn new_order_id(id: Int) -> OrderId {
OrderId(id)
}
pub fn order_id_to_int(id: OrderId) -> Int {
let OrderId(value) = id
value
}
// Builder pattern with opaque types
pub opaque type Query {
Query(table: String, conditions: List(String), limit: Option(Int))
}
pub fn new_query(table: String) -> Query {
Query(table: table, conditions: [], limit: None)
}
pub fn where(query: Query, condition: String) -> Query {
let Query(table: table, conditions: conditions, limit: limit) = query
Query(table: table, conditions: [condition, ..conditions], limit: limit)
}
pub fn limit(query: Query, n: Int) -> Query {
let Query(table: table, conditions: conditions, limit: _) = query
Query(table: table, conditions: conditions, limit: Some(n))
}
pub fn to_sql(query: Query) -> String {
let Query(table: table, conditions: conditions, limit: limit) = query
let where_clause = case conditions {
[] -> ""
_ -> " WHERE " <> string.join(conditions, " AND ")
}
let limit_clause = case limit {
None -> ""
Some(n) -> " LIMIT " <> int.to_string(n)
}
"SELECT * FROM " <> table <> where_clause <> limit_clause
}
Type aliases improve readability while opaque types enforce invariants and hide implementation details.
Best Practices
Use custom types for domain modeling to make invalid states unrepresentable at compile time
Leverage pattern matching exhaustiveness to ensure all cases are handled without runtime checks
Prefer Result over exceptions for expected errors to make error handling explicit
Use opaque types for validation to prevent creating invalid values outside the module
Apply generic types when algorithms work across multiple types to maximize code reuse
Use type aliases for complex types to improve readability and maintainability
Pattern match on specific variants rather than using catch-all patterns for safety
Use phantom types for compile-time state tracking in state machines or workflows
Avoid nested Results by using result.try or use expressions for cleaner error handling
Document opaque type invariants to clarify constraints enforced by the abstraction
Common Pitfalls
Overusing generic types adds complexity without benefits when specific types suffice
Not using opaque types exposes internal representation and breaks encapsulation
Ignoring compiler warnings about non-exhaustive patterns leads to runtime crashes
Creating redundant type aliases for simple types reduces clarity
Not validating at boundaries when using opaque types allows invalid data creation
Using underscore in patterns excessively misses valuable destructuring opportunities
Nesting too many Results creates callback-like complexity; use use expressions
Not using guards in patterns when conditions are needed causes verbose case expressions
Creating overly complex ADTs with too many variants reduces maintainability
Forgetting type annotations on public functions reduces documentation clarity
When to Use This Skill
Apply custom types when modeling domain entities with specific fields and behaviors.
Use ADTs when data can exist in multiple states or variants with different properties.
Leverage pattern matching for all conditional logic requiring destructuring or exhaustiveness.
Apply generic types when implementing reusable algorithms or data structures.
Use opaque types when enforcing invariants or hiding implementation details from module users.
Employ Result types for all operations that can fail to make error handling explicit.