Convert Scala to Roc

Convert Scala to Roc

Convert Scala code to idiomatic Roc. This skill extends meta-convert-dev with Scala-to-Roc specific type mappings, idiom translations, and architectural patterns for moving from JVM-based functional programming to platform-based pure functional programming.

This Skill Extends

• meta-convert-dev - Foundational conversion patterns (APTV workflow, testing strategies)

For general concepts like the Analyze → Plan → Transform → Validate workflow, testing strategies, and common pitfalls, see the meta-skill first.

This Skill Adds

• Type mappings: Scala JVM types → Roc static types
• Paradigm translation: Object-functional hybrid → Pure functional with platform separation
• Idiom translations: Scala patterns → Roc functional patterns
• Error handling: Exceptions + Try/​Either → Result types
• Concurrency: Futures/​Actors → Platform Tasks
• Module system: Scala packages/​objects → Roc platform/​application architecture
• Type classes: Scala implicits/​given → Roc abilities

This Skill Does NOT Cover

• General conversion methodology - see meta-convert-dev
• Scala language fundamentals - see lang-scala-dev
• Roc language fundamentals - see lang-roc-dev
• Reverse conversion (Roc → Scala) - see convert-roc-scala

---

Quick Reference

When Converting Code

1. Analyze JVM semantics before writing Roc
2. Identify effect boundaries - separate pure logic from I/​O
3. Map object hierarchies to data - classes become records, inheritance becomes composition
4. Redesign for immutability - Scala's var becomes Roc's pure transformation
5. Extract pure functions - separate computation from effects
6. Test equivalence - verify behavior matches despite architectural differences

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Paradigm Translation

Mental Model Shift: Object-Functional → Pure Functional + Platform

Functional Paradigm Alignment

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Type System Mapping

Primitive Types

Collection Types

Composite Types

Function Types

---

Idiom Translation

Pattern 1: Simple Function and Case Class

Scala:

case class User(name: String, age: Int, email: String)

object User {
  def create(name: String, age: Int, email: String): User = {
    User(name, age, email)
  }

  def greet(user: User): String = {
    s"Hello, ${user.name}! You are ${user.age} years old."
  }
}

Roc:

interface User
    exposes [User, create, greet]
    imports []

User : {
    name : Str,
    age : U32,
    email : Str,
}

create : Str, U32, Str -> User
create = \name, age, email ->
    { name, age, email }

greet : User -> Str
greet = \{ name, age } ->
    "Hello, \(name)! You are \(Num.toStr(age)) years old."

Why this translation:

• Scala case class → Roc record type
• Companion object → Roc interface (module)
• String interpolation syntax differs
• Type inference works in both

Pattern 2: Sealed Trait ADT with Pattern Matching

Scala:

sealed trait Result[+A]
case class Success[A](value: A) extends Result[A]
case class Failure(error: String) extends Result[Nothing]
case object Pending extends Result[Nothing]

def handle[A](result: Result[A]): String = result match {
  case Success(value) => s"Got: $value"
  case Failure(error) => s"Error: $error"
  case Pending => "Waiting..."
}

Roc:

Result a : [Success a, Failure Str, Pending]

handle : Result a -> Str where a implements Inspect
handle = \result ->
    when result is
        Success(value) -> "Got: \(Inspect.toStr(value))"
        Failure(error) -> "Error: \(error)"
        Pending -> "Waiting..."

Why this translation:

• Sealed trait → Tag union
• Case classes → Tags with payloads
• Case object → Tag without payload
• Pattern matching syntax very similar
• Roc enforces exhaustiveness at compile time

Pattern 3: Option Handling

Scala:

def findUser(id: Int, users: List[User]): Option[User] = {
  users.find(_.id == id)
}

def getEmail(maybeUser: Option[User]): String = {
  maybeUser.map(_.email).getOrElse("no email")
}

// For-comprehension
def combineUsers(id1: Int, id2: Int): Option[(User, User)] = {
  for {
    user1 <- findUser(id1, users)
    user2 <- findUser(id2, users)
  } yield (user1, user2)
}

Roc:

findUser : U64, List User -> [Some User, None]
findUser = \id, users ->
    users
    |> List.findFirst(\user -> user.id == id)
    |> Result.map(Some)
    |> Result.withDefault(None)

getEmail : [Some User, None] -> Str
getEmail = \maybeUser ->
    when maybeUser is
        Some({ email }) -> email
        None -> "no email"

# Nested when for comprehension-like flow
combineUsers : U64, U64, List User -> [Some (User, User), None]
combineUsers = \id1, id2, users ->
    when findUser(id1, users) is
        Some(user1) ->
            when findUser(id2, users) is
                Some(user2) -> Some((user1, user2))
                None -> None
        None -> None

Why this translation:

• Scala Option → Roc tag union [Some a, None]
• map/getOrElse → pattern matching or Result helpers
• For-comprehension → nested when expressions
• More verbose but explicit

Pattern 4: List Processing

Scala:

val numbers = List(1, 2, 3, 4, 5)

val doubled = numbers.map(_ * 2)
val evens = numbers.filter(_ % 2 == 0)
val sum = numbers.foldLeft(0)(_ + _)

// List comprehension
val squares = for {
  x <- numbers
  if x % 2 == 0
} yield x * x

Roc:

numbers = [1, 2, 3, 4, 5]

doubled = List.map(numbers, \n -> n * 2)
evens = List.keepIf(numbers, \n -> n % 2 == 0)
sum = List.walk(numbers, 0, Num.add)

# List comprehension becomes pipeline
squares = numbers
    |> List.keepIf(\x -> x % 2 == 0)
    |> List.map(\x -> x * x)

Why this translation:

• Similar higher-order functions
• foldLeft → List.walk
• filter → List.keepIf
• For-comprehension → pipeline with map/​filter
• Roc uses explicit function composition

Pattern 5: Error Handling with Either/​Try

Scala:

def divide(a: Int, b: Int): Either[String, Int] = {
  if (b == 0) Left("Division by zero")
  else Right(a / b)
}

def calculate(a: Int, b: Int, c: Int): Either[String, Int] = {
  for {
    x <- divide(a, b)
    y <- divide(x, c)
  } yield y
}

// Try for exceptions
import scala.util.{Try, Success, Failure}

def parseInt(s: String): Try[Int] = Try(s.toInt)

def safeParse(s: String): Option[Int] = parseInt(s).toOption

Roc:

divide : I64, I64 -> Result I64 [DivByZero]
divide = \a, b ->
    if b == 0 then
        Err(DivByZero)
    else
        Ok(a // b)

calculate : I64, I64, I64 -> Result I64 [DivByZero]
calculate = \a, b, c ->
    x = divide!(a, b)
    y = divide!(x, c)
    Ok(y)

# Try equivalent
parseInt : Str -> Result I64 [ParseError]
parseInt = \s ->
    when Str.toI64(s) is
        Ok(n) -> Ok(n)
        Err(_) -> Err(ParseError)

safeParse : Str -> [Some I64, None]
safeParse = \s ->
    when parseInt(s) is
        Ok(n) -> Some(n)
        Err(_) -> None

Why this translation:

• Either[L, R] → Result ok err (note: order reversed)
• For-comprehension → try operator ! for early returns
• Try → Result with explicit error types
• toOption → pattern matching to convert Result → Option-like tag union

Pattern 6: Trait and Implicits to Abilities

Scala:

trait Show[A] {
  def show(a: A): String
}

object Show {
  implicit val intShow: Show[Int] = (a: Int) => a.toString
  implicit val stringShow: Show[String] = (a: String) => s"'$a'"
}

def print[A](a: A)(implicit s: Show[A]): Unit = {
  println(s.show(a))
}

print(42)      // Uses intShow
print("hello") // Uses stringShow

Roc:

# Roc abilities are automatic for basic types
# For custom behavior, use functions with ability constraints

toString : a -> Str where a implements Inspect
toString = \value ->
    Inspect.toStr(value)

# Usage - Inspect is automatically implemented
expect toString(42) == "42"
expect toString("hello") == "\"hello\""

# For custom types, abilities are derived automatically
User : { name : Str, age : U32 }
user = { name: "Alice", age: 30 }

expect Inspect.toStr(user) == "{ name: \"Alice\", age: 30 }"

Why this translation:

• Scala trait → Roc ability
• Implicit instances → automatic derivation for records/​tags
• Type class pattern → ability constraint where a implements Ability
• Roc has fewer built-in abilities but they're more automatic

Pattern 7: Higher-Order Functions and Currying

Scala:

def applyTwice[A](f: A => A, x: A): A = f(f(x))

def add(a: Int)(b: Int): Int = a + b
val add5 = add(5) _

def compose[A, B, C](f: B => C, g: A => B): A => C = {
  a => f(g(a))
}

Roc:

applyTwice : (a -> a), a -> a
applyTwice = \f, x ->
    f(f(x))

# Currying in Roc requires explicit function return
add : I64 -> (I64 -> I64)
add = \a ->
    \b -> a + b

add5 = add(5)

compose : (b -> c), (a -> b) -> (a -> c)
compose = \f, g ->
    \a -> f(g(a))

Why this translation:

• Higher-order functions work similarly
• Currying must be explicit in Roc (return a function)
• Function composition same concept
• Type signatures use arrows consistently

Pattern 8: Records with Update

Scala:

case class Config(
  host: String,
  port: Int,
  timeout: Int = 5000,
  retries: Int = 3
)

val config = Config("localhost", 8080)
val updated = config.copy(port = 9090, retries = 5)

Roc:

Config : {
    host : Str,
    port : U16,
    timeout : U32,
    retries : U32,
}

defaultConfig : Str, U16 -> Config
defaultConfig = \host, port ->
    {
        host,
        port,
        timeout: 5000,
        retries: 3,
    }

config = defaultConfig("localhost", 8080)
updated = { config &
    port: 9090,
    retries: 5,
}

Why this translation:

• Case class copy → Roc record update { record & field: value }
• Default parameters → constructor function with defaults
• Immutable updates work similarly
• Roc update syntax is explicit

---

Concurrency Patterns

Scala Future vs Roc Task

Scala uses Futures for async computation on the JVM. Roc delegates all concurrency to the platform.

Scala:

import scala.concurrent.Future
import scala.concurrent.ExecutionContext.Implicits.global

def fetchUser(id: Int): Future[User] = Future {
  // Async operation
  database.query(s"SELECT * FROM users WHERE id = $id")
}

def fetchPosts(userId: Int): Future[List[Post]] = Future {
  database.query(s"SELECT * FROM posts WHERE author = $userId")
}

// Composition
val result: Future[(User, List[Post])] = for {
  user <- fetchUser(123)
  posts <- fetchPosts(user.id)
} yield (user, posts)

// Parallel execution
val users: Future[List[User]] = Future.sequence(
  List(1, 2, 3).map(fetchUser)
)

Roc:

import pf.Task exposing [Task]
import pf.Database

# Platform provides Task type
fetchUser : U64 -> Task User [DbErr]
fetchUser = \id ->
    Database.query!("SELECT * FROM users WHERE id = \(Num.toStr(id))")

fetchPosts : U64 -> Task (List Post) [DbErr]
fetchPosts = \userId ->
    Database.query!("SELECT * FROM posts WHERE author = \(Num.toStr(userId))")

# Sequential composition using !
result : Task (User, List Post) [DbErr]
result =
    user = fetchUser!(123)
    posts = fetchPosts!(user.id)
    Task.ok((user, posts))

# Platform provides parallel primitives
users : Task (List User) [DbErr]
users =
    Task.sequence([
        fetchUser(1),
        fetchUser(2),
        fetchUser(3),
    ])

Why this translation:

• Scala Future → Roc platform Task
• For-comprehension → try operator ! with Task
• ExecutionContext → handled by platform
• Parallel execution → platform-provided primitives
• Roc apps stay pure, platform handles concurrency

Scala Actors (Akka) vs Roc Platform

Scala (Akka Typed):

import akka.actor.typed._
import akka.actor.typed.scaladsl.Behaviors

sealed trait CounterMsg
case object Increment extends CounterMsg
case class GetCount(replyTo: ActorRef[Int]) extends CounterMsg

def counter(count: Int): Behavior[CounterMsg] =
  Behaviors.receive { (context, message) =>
    message match {
      case Increment =>
        counter(count + 1)
      case GetCount(replyTo) =>
        replyTo ! count
        Behaviors.same
    }
  }

Roc:

# Roc has no built-in actors
# Design as pure state machine

State : I64

init : State
init = 0

increment : State -> State
increment = \count ->
    count + 1

getCount : State -> I64
getCount = \count ->
    count

# Platform would provide state management if needed
# Application code remains pure

Why this translation:

• Actors → pure state functions
• Message passing → function parameters
• State mutation → new state returned
• Platform handles concurrency, not application
• Simpler mental model: data transformation, not processes

---

Module System Translation

Scala Package/​Object → Roc Interface

Scala:

package com.example.users

case class User(id: Int, name: String, email: String)

object UserService {
  def create(name: String, email: String): User = {
    val id = generateId()
    User(id, name, email)
  }

  def validate(user: User): Either[String, User] = {
    if (user.email.contains("@")) Right(user)
    else Left("Invalid email")
  }
}

Roc:

interface UserService
    exposes [User, create, validate]
    imports []

User : {
    id : U64,
    name : Str,
    email : Str,
}

create : Str, Str -> User
create = \name, email ->
    id = generateId({})
    { id, name, email }

validate : User -> Result User [InvalidEmail]
validate = \user ->
    if Str.contains(user.email, "@") then
        Ok(user)
    else
        Err(InvalidEmail)

# Private helper (not in exposes)
generateId : {} -> U64
generateId = \{} ->
    # Implementation
    123

Why this translation:

• Package → Roc module structure (file organization)
• Companion object → Interface exposing functions
• Private members → not in exposes list
• Public API → explicitly listed in exposes

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Common Pitfalls

1. Trying to Use Mutable State

Scala (Anti-pattern in Roc):

var counter = 0
def increment(): Unit = { counter += 1 }

Roc Approach:

# No mutable state - return new value
increment : I64 -> I64
increment = \counter ->
    counter + 1

# Usage
counter = 0
newCounter = increment(counter)

Why: Roc has no mutable variables. Always return new values.

2. Expecting JVM Collections Performance Characteristics

Pitfall: Assuming Scala Vector performance in Roc.

Solution: Roc List is the primary collection. It's efficient for most use cases. Don't over-optimize based on JVM knowledge.

3. Trying to Use Null

Scala:

var maybeUser: User = null  // Avoid!
val user: Option[User] = Option(nullableValue)

Roc:

# No null! Use tag unions
maybeUser : [Some User, None]
maybeUser = None

# When converting from nullable source
userFromNullable : [Some User, None]
userFromNullable = Some({ name: "Alice", age: 30 })

Why: Roc has no null. Always use tag unions for optional values.

4. Confusing Either Order

Pitfall: Scala Either[L, R] vs Roc Result ok err

Scala:

val result: Either[String, Int] = Right(42)  // Right is success

Roc:

result : Result I64 Str  # First param is success, second is error
result = Ok(42)

Why: Roc Result has opposite parameter order compared to Scala Either.

5. Expecting Implicit Conversions

Pitfall: Scala's implicit conversions don't exist in Roc.

Solution: All conversions must be explicit:

# Explicit conversion required
intToStr : I64 -> Str
intToStr = Num.toStr

str = intToStr(42)

6. Forgetting Platform Separation

Pitfall: Trying to do I/​O directly in application code.

Solution: Use platform-provided Tasks:

# Wrong - no direct I/​O
# readFile("path")  # This doesn't exist!

# Correct - platform Task
import pf.File
import pf.Task exposing [Task]

readFile : Str -> Task Str [FileErr]
readFile = \path ->
    File.readUtf8(path)

Why: Roc applications are pure. All effects go through the platform.

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Tooling

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Examples

Example 1: Simple HTTP Client

Scala (Akka HTTP):

import akka.actor.ActorSystem
import akka.http.scaladsl.Http
import akka.http.scaladsl.model._
import scala.concurrent.Future

implicit val system = ActorSystem()
import system.dispatcher

def fetchUrl(url: String): Future[String] = {
  Http().singleRequest(HttpRequest(uri = url)).flatMap { response =>
    response.entity.toStrict(5.seconds).map(_.data.utf8String)
  }
}

val content: Future[String] = fetchUrl("https://example.com")

Roc (basic-cli platform):

app [main] {
    pf: platform "https://github.com/​roc-lang/​basic-cli/​releases/​download/​0.10.0/​vNe6s9hWzoTZtFmNkvEICPErI9ptji_ySjicO6CkucY.tar.br"
}

import pf.Http
import pf.Task exposing [Task]
import pf.Stdout

fetchUrl : Str -> Task Str [HttpErr]
fetchUrl = \url ->
    response = Http.get!(url)
    Task.ok(response.body)

main : Task {} []
main =
    content = fetchUrl!("https://example.com")
    Stdout.line!(content)

Example 2: Data Processing Pipeline

Scala:

case class User(id: Int, name: String, age: Int, active: Boolean)

val users = List(
  User(1, "Alice", 30, true),
  User(2, "Bob", 25, false),
  User(3, "Charlie", 35, true)
)

val activeUserNames = users
  .filter(_.active)
  .filter(_.age >= 30)
  .map(_.name)
  .sorted

// Result: List("Alice", "Charlie")

Roc:

User : { id : U64, name : Str, age : U32, active : Bool }

users = [
    { id: 1, name: "Alice", age: 30, active: Bool.true },
    { id: 2, name: "Bob", age: 25, active: Bool.false },
    { id: 3, name: "Charlie", age: 35, active: Bool.true },
]

activeUserNames = users
    |> List.keepIf(\user -> user.active)
    |> List.keepIf(\user -> user.age >= 30)
    |> List.map(\user -> user.name)
    |> List.sortAsc

# Result: ["Alice", "Charlie"]

Example 3: Error Handling Pipeline

Scala:

def parseAndDivide(aStr: String, bStr: String): Either[String, Int] = {
  for {
    a <- aStr.toIntOption.toRight(s"Invalid a: $aStr")
    b <- bStr.toIntOption.toRight(s"Invalid b: $bStr")
    result <- if (b != 0) Right(a / b) else Left("Division by zero")
  } yield result
}

parseAndDivide("10", "2")  // Right(5)
parseAndDivide("10", "0")  // Left("Division by zero")
parseAndDivide("abc", "2") // Left("Invalid a: abc")

Roc:

parseAndDivide : Str, Str -> Result I64 [InvalidA, InvalidB, DivByZero]
parseAndDivide = \aStr, bStr ->
    a =
        when Str.toI64(aStr) is
            Ok(n) -> Ok(n)
            Err(_) -> Err(InvalidA)

    b =
        when Str.toI64(bStr) is
            Ok(n) -> Ok(n)
            Err(_) -> Err(InvalidB)

    # Using try operator for early returns
    aVal = a!
    bVal = b!

    if bVal == 0 then
        Err(DivByZero)
    else
        Ok(aVal // bVal)

expect parseAndDivide("10", "2") == Ok(5)
expect parseAndDivide("10", "0") == Err(DivByZero)
expect parseAndDivide("abc", "2") == Err(InvalidA)

---

Performance Considerations

Scala vs Roc Performance Differences

Optimization Tips

1. Don't over-optimize based on JVM knowledge - Roc's performance profile is different
2. Trust List performance - It's the primary collection and is well-optimized
3. Leverage platform capabilities - Let platform handle concurrency and I/​O
4. Profile before optimizing - Different bottlenecks than JVM code
5. Avoid premature abstraction - Roc encourages simple, direct code

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See Also

For more examples and patterns, see:

• meta-convert-dev - Foundational patterns with cross-language examples
• lang-scala-dev - Scala development patterns
• lang-roc-dev - Roc development patterns
• convert-erlang-roc - Similar functional language conversion (BEAM → Roc)

Cross-cutting pattern skills:

• patterns-concurrency-dev - Futures/​Actors vs Tasks across languages
• patterns-serialization-dev - JSON, validation across languages
• patterns-metaprogramming-dev - Implicits vs abilities vs other approaches