convert-clojure-scala

name	convert-clojure-scala
description	Bidirectional conversion between Clojure and Scala. Use when migrating projects between these languages in either direction. Extends meta-convert-dev with Clojure↔Scala specific patterns. Use when migrating Clojure projects to Scala, translating Clojure patterns to idiomatic Scala, or refactoring Clojure codebases. Extends meta-convert-dev with Clojure-to-Scala specific patterns.

Clojure ↔ Scala Conversion

Bidirectional conversion between Clojure and Scala. This skill extends meta-convert-dev with Clojure↔Scala specific type mappings, idiom translations, and tooling for converting functional code between dynamic Lisp and statically-typed hybrid FP/OOP.

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: Clojure dynamic types → Scala static types with inference
• Idiom translations: Clojure Lisp-style → idiomatic Scala hybrid FP/OOP
• Error handling: Clojure exceptions/maps → Scala Option/Either/Try
• Async patterns: Clojure core.async/futures → Scala Futures/Akka/Cats Effect
• Concurrency models: STM/agents → actors/STM alternatives
• Platform translation: JVM (Clojure) → JVM (Scala) with better performance

This Skill Does NOT Cover

• General conversion methodology - see meta-convert-dev
• Clojure language fundamentals - see lang-clojure-dev
• Scala language fundamentals - see lang-scala-dev

---

Quick Reference

Clojure	Scala	Notes
String	String	Direct mapping (both JVM)
Long (default int)	Int / Long	Scala Int more common; Long for large
Double (default decimal)	Double	Direct mapping
Boolean	Boolean	true/false in both
nil	None / null	Prefer Option[A] over null
'(...) list	List[A]	Immutable linked list
[...] vector	Vector[A] / List[A]	Vector for indexed, List for sequential
{...} map	Map[K, V]	Immutable map
#{...} set	Set[A]	Immutable set
(fn [x] ...)	(x: A) => B	Lambda/anonymous function
defn	def name(...)	Named function
defrecord	case class	Data structure with methods
Multimethod	Trait + pattern matching	Polymorphic dispatch
Protocol	Trait	Behavior contract
Atom	AtomicReference / Ref	Mutable reference
Agent	Akka actor / Future	Async computation
core.async channel	Akka Stream / FS2	Stream processing

When Converting Code

1. Analyze source thoroughly before writing target - understand Clojure semantics
2. Map types first - create explicit type mapping table (dynamic → static)
3. Preserve semantics over syntax similarity
4. Adopt Scala idioms - don't write "Clojure code in Scala syntax"
5. Handle edge cases - nil-safety, lazy evaluation, type erasure
6. Test equivalence - same inputs → same outputs
7. Embrace static typing - leverage compiler for correctness
8. Consider performance - Scala can be more performant with proper types

---

Type System Mapping

Primitive Types

Clojure	Scala	Notes
Boolean (true/false)	Boolean	Direct mapping
Long (default integer)	Int	Scala Int (32-bit) is more common
Long (large integers)	Long	Use Long for 64-bit integers
BigInt	BigInt	Arbitrary precision integers
Double (default decimal)	Double	Direct mapping
BigDecimal	BigDecimal	Arbitrary precision decimals
Character	Char	Single character
String	String	Immutable strings (JVM)
nil	null / None	Prefer Option[A] over null
Keyword :key	Symbol('key) / String	Use sealed traits or enums for tagged types
Symbol 'sym	No direct equivalent	Use case objects or sealed traits
Ratio 1/3	No direct equivalent	Use Rational library or Double

Collection Types

Clojure	Scala	Notes
'(1 2 3) list	List(1, 2, 3)	Immutable singly-linked list
[1 2 3] vector	Vector(1, 2, 3)	Indexed immutable sequence
{:a 1 :b 2} map	Map("a" -> 1, "b" -> 2)	Immutable hash map
#{1 2 3} set	Set(1, 2, 3)	Immutable hash set
Lazy seq	LazyList / Iterator	Lazy evaluation
Transient	Mutable collections	Use scala.collection.mutable temporarily
Persistent	Immutable collections	Default in Scala
Java array	Array[A]	Mutable, fixed-size

Composite Types

Clojure	Scala	Notes
Plain map {:name "Alice"}	case class User(name: String)	Prefer case classes for structured data
Plain map (open)	Map[String, Any]	When structure is truly dynamic
defrecord	case class	Data structure with protocol implementations
Tagged map {:type :circle}	sealed trait + case classes	ADT with pattern matching
Multimethod dispatch	Pattern matching	Type-based dispatch
Protocol	Trait	Interface with possible default implementations
deftype	class + trait	Low-level performance-critical types
Namespace	object (singleton)	Module-level functions/values

Function Types

Clojure	Scala	Notes
(fn [x] body)	(x: A) => B	Anonymous function
(fn [x y] body)	(x: A, y: B) => C	Multi-parameter function
#(+ % 1)	_ + 1	Placeholder syntax
Variadic [& args]	args: A*	Variable arguments
Multi-arity fn	Overloaded methods	Multiple parameter lists
Curried (manual)	Curried def f(x: A)(y: B)	Automatic currying in Scala

Nil/Null Handling

Clojure	Scala	Notes
nil	None	Absence of value
Value	Some(value)	Present value
Check (nil? x)	x.isEmpty	Pattern matching preferred
(some? x)	x.isDefined	Check for presence
(or x default)	x.getOrElse(default)	Default value
(when-let [x ...] ...)	for { x <- opt } yield ...	For-comprehension
(some-> x f g)	x.map(f).map(g)	Chained operations

---

Idiom Translation

Pattern 1: nil → Option Type

Clojure:

(defn get-user [id]
  ;; Returns nil if not found
  (get @users-db id))

(defn get-user-email [id]
  (when-let [user (get-user id)]
    (:email user)))

;; With default
(defn get-user-name [id]
  (if-let [user (get-user id)]
    (:name user)
    "Unknown"))

Scala:

def getUser(id: Int): Option[User] = {
  usersDb.get(id)
}

def getUserEmail(id: Int): Option[String] = {
  getUser(id).map(_.email)
}

// With default
def getUserName(id: Int): String = {
  getUser(id).map(_.name).getOrElse("Unknown")
}

// Pattern matching
def getUserNameMatch(id: Int): String = getUser(id) match {
  case Some(user) => user.name
  case None => "Unknown"
}

Why this translation:

• Clojure nil → Scala None (explicit absence)
• Clojure when-let → Scala .map() combinator
• Clojure if-let with default → Scala .getOrElse()
• Pattern matching is more idiomatic in Scala than if/else chains
• Compiler enforces handling of both Some and None cases

Pattern 2: Maps → Case Classes

Clojure:

(def user {:name "Alice" :age 30 :email "[e-mail]"})

(defn greet [user]
  (str "Hello, " (:name user)))

(defn is-adult? [user]
  (>= (:age user) 18))

;; Update
(def updated-user (assoc user :age 31))

Scala:

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

val user = User("Alice", 30, "[e-mail]")

def greet(user: User): String = {
  s"Hello, ${user.name}"
}

def isAdult(user: User): Boolean = {
  user.age >= 18
}

// Update (immutable copy)
val updatedUser = user.copy(age = 31)

Why this translation:

• Clojure maps → Scala case classes for structured data
• Keyword access :key → Field access .field
• Clojure assoc → Scala .copy() method
• Case classes provide:
  • Compile-time field checking
  • Pattern matching support
  • Automatic equals, hashCode, toString
  • Better IDE support and refactoring

Pattern 3: Threading Macros → Method Chaining

Clojure:

(defn process-items [items]
  (->> items
       (filter :active)
       (map :value)
       (filter #(> % 10))
       (reduce +)))

;; Thread-first
(defn transform-user [user]
  (-> user
      (assoc :normalized-name (clojure.string/lower-case (:name user)))
      (update :age inc)
      (dissoc :temp-field)))

Scala:

def processItems(items: List[Item]): Int = {
  items
    .filter(_.active)
    .map(_.value)
    .filter(_ > 10)
    .sum
}

// Or with for-comprehension
def processItemsFor(items: List[Item]): Int = {
  (for {
    item <- items if item.active
    value = item.value if value > 10
  } yield value).sum
}

// Thread-first style
def transformUser(user: User): User = {
  user
    .copy(normalizedName = user.name.toLowerCase)
    .copy(age = user.age + 1)
}

Why this translation:

• Clojure ->> (thread-last) → Scala method chaining (data flows left-to-right)
• Clojure -> (thread-first) → Scala .copy() chaining for updates
• Scala for-comprehensions are alternative for complex filter/map chains
• Method chaining is more natural in Scala due to OOP foundation
• Both styles maintain immutability

Pattern 4: Multimethods → Pattern Matching / Type Classes

Clojure:

(defmulti area :type)

(defmethod area :circle [{:keys [radius]}]
  (* Math/PI radius radius))

(defmethod area :rectangle [{:keys [width height]}]
  (* width height))

(defmethod area :triangle [{:keys [base height]}]
  (* 0.5 base height))

;; Usage
(area {:type :circle :radius 5})

Scala:

// Approach 1: Sealed trait + pattern matching (most idiomatic)
sealed trait Shape
case class Circle(radius: Double) extends Shape
case class Rectangle(width: Double, height: Double) extends Shape
case class Triangle(base: Double, height: Double) extends Shape

def area(shape: Shape): Double = shape match {
  case Circle(r) => math.Pi * r * r
  case Rectangle(w, h) => w * h
  case Triangle(b, h) => 0.5 * b * h
}

// Approach 2: Polymorphic method (OOP style)
sealed trait Shape {
  def area: Double
}

case class Circle(radius: Double) extends Shape {
  def area: Double = math.Pi * radius * radius
}

case class Rectangle(width: Double, height: Double) extends Shape {
  def area: Double = width * height
}

case class Triangle(base: Double, height: Double) extends Shape {
  def area: Double = 0.5 * base * height
}

// Usage
val circle = Circle(5)
area(circle)  // Approach 1
circle.area   // Approach 2

Why this translation:

• Clojure multimethods → Scala sealed traits + pattern matching (type-safe dispatch)
• Clojure :type key → Scala case class type (compiler-checked)
• Pattern matching exhaustiveness checked at compile time
• Alternative: polymorphic methods for OOP-style dispatch
• Sealed traits ensure all cases are known at compile time

Pattern 5: Atoms → AtomicReference / Ref

Clojure:

(def counter (atom 0))

(swap! counter inc)
(swap! counter + 5)
(reset! counter 0)

@counter  ;; Deref

;; With validation
(def validated-atom
  (atom 0
    :validator #(>= % 0)))

Scala:

import java.util.concurrent.atomic.AtomicReference

val counter = new AtomicReference(0)

counter.updateAndGet(_ + 1)
counter.updateAndGet(_ + 5)
counter.set(0)

counter.get()  // Deref

// With Cats STM
import cats.effect.IO
import cats.effect.std.Ref

val program = for {
  counter <- Ref[IO].of(0)
  _ <- counter.update(_ + 1)
  _ <- counter.update(_ + 5)
  _ <- counter.set(0)
  value <- counter.get
} yield value

// Or use synchronized for simple cases
class Counter {
  private var count = 0

  def increment(): Int = synchronized {
    count += 1
    count
  }

  def get: Int = synchronized(count)
}

Why this translation:

• Clojure atom → Scala AtomicReference for thread-safe mutable state
• Clojure swap! → Scala .updateAndGet()
• Clojure reset! → Scala .set()
• Clojure @atom → Scala .get()
• For functional effects, use Cats Effect Ref[IO]
• Validation can be added through wrapper methods

Pattern 6: core.async Channels → Akka Streams / FS2

Clojure:

(require '[clojure.core.async :as async :refer [go <! >! chan]])

(defn process-messages []
  (let [ch (chan 10)]
    (go
      (loop []
        (when-let [msg (<! ch)]
          (println "Processing:" msg)
          (recur))))
    ch))

(def ch (process-messages))
(go (>! ch "Hello"))

Scala:

// Approach 1: Akka Streams
import akka.stream._
import akka.stream.scaladsl._
import akka.actor.ActorSystem

implicit val system = ActorSystem()
implicit val materializer = ActorMaterializer()

val source = Source.queue[String](bufferSize = 10, OverflowStrategy.backpressure)

val (queue, _) = source
  .map { msg =>
    println(s"Processing: $msg")
    msg
  }
  .toMat(Sink.ignore)(Keep.both)
  .run()

queue.offer("Hello")

// Approach 2: FS2 (functional streams)
import cats.effect.IO
import fs2._

val stream = Stream.eval(IO(println("Processing: Hello")))
stream.compile.drain.unsafeRunSync()

// Approach 3: Simple Future-based
import scala.concurrent.Future
import scala.concurrent.ExecutionContext.Implicits.global

def processMessages(): String => Future[Unit] = { msg =>
  Future {
    println(s"Processing: $msg")
  }
}

val processor = processMessages()
processor("Hello")

Why this translation:

• Clojure core.async channels → Akka Streams for back-pressure and complex flows
• Clojure go blocks → Scala Futures or FS2 streams
• Akka Streams provide more structure and operators
• FS2 integrates with Cats Effect for pure FP
• Choose based on complexity: Futures (simple), Akka (complex), FS2 (pure FP)

Pattern 7: Macros → Inline Methods / Compile-Time

Clojure:

(defmacro unless [condition & body]
  `(if (not ~condition)
     (do ~@body)))

(unless false
  (println "This runs")
  "result")

;; Infix macro
(defmacro infix [a op b]
  `(~op ~a ~b))

(infix 3 + 5)  ;; => 8

Scala:

// Scala 3: Inline methods (simpler than macros)
inline def unless(condition: Boolean)(body: => Unit): Unit = {
  if (!condition) body
}

unless(false) {
  println("This runs")
  "result"
}

// Scala 2: By-name parameters
def unless2(condition: Boolean)(body: => Unit): Unit = {
  if (!condition) body
}

// For infix, use operators
extension (a: Int) {
  infix def plus(b: Int): Int = a + b
}

3 plus 5  // => 8

// Scala 3 macros (for complex metaprogramming)
import scala.quoted.*

inline def debug(inline expr: Any): Any = ${
  debugImpl('expr)
}

def debugImpl(expr: Expr[Any])(using Quotes): Expr[Any] = {
  import quotes.reflect.*
  val tree = expr.asTerm
  val code = tree.show
  '{
    println(s"$code => ${$expr}")
    $expr
  }
}

Why this translation:

• Clojure macros → Scala inline methods (Scala 3) for simple cases
• Complex metaprogramming → Scala 3 macros (quote/splice)
• By-name parameters => A for lazy evaluation (similar to macro delay)
• Extension methods for DSL-like syntax
• Scala macros are more restrictive but type-safe
• Prefer higher-order functions over macros when possible

Pattern 8: Protocols → Traits

Clojure:

(defprotocol Drawable
  (draw [this]))

(defrecord Circle [radius]
  Drawable
  (draw [this]
    (str "Drawing circle with radius " radius)))

(defrecord Rectangle [width height]
  Drawable
  (draw [this]
    (str "Drawing rectangle " width "x" height)))

;; Extend to existing types
(extend-type String
  Drawable
  (draw [this]
    (str "Drawing text: " this)))

Scala:

trait Drawable {
  def draw: String
}

case class Circle(radius: Double) extends Drawable {
  def draw: String = s"Drawing circle with radius $radius"
}

case class Rectangle(width: Double, height: Double) extends Drawable {
  def draw: String = s"Drawing rectangle ${width}x${height}"
}

// Extension methods for existing types (Scala 3)
extension (s: String) {
  def draw: String = s"Drawing text: $s"
}

// Or implicit class (Scala 2)
implicit class DrawableString(s: String) extends Drawable {
  def draw: String = s"Drawing text: $s"
}

// Usage
val circle: Drawable = Circle(5)
circle.draw

"Hello".draw  // Extension method

Why this translation:

• Clojure protocols → Scala traits (interfaces with implementations)
• Clojure defrecord with protocol → Scala case class extending trait
• Clojure extend-type → Scala extension methods or implicit classes
• Scala traits support default implementations
• Extension methods don't require wrapper types
• Traits can have type parameters and self-types

---

Error Handling

Clojure Error Model → Scala Error Model

Clojure primarily uses exceptions with ex-info for structured errors. Scala offers typed error handling with Option, Either, and Try.

Clojure exception pattern:

(defn divide [a b]
  (if (zero? b)
    (throw (ex-info "Division by zero" {:numerator a}))
    (/ a b)))

(defn safe-divide [a b]
  (try
    {:ok (divide a b)}
    (catch Exception e
      {:error (.getMessage e) :data (ex-data e)})))

Scala typed error pattern:

// Option for simple presence/absence
def divide(a: Int, b: Int): Option[Int] = {
  if (b == 0) None
  else Some(a / b)
}

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

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

def divideTry(a: Int, b: Int): Try[Int] = Try {
  if (b == 0) throw new ArithmeticException("Division by zero")
  a / b
}

// Custom error type (recommended for domain errors)
sealed trait DivisionError
case object DivisionByZero extends DivisionError
case class InvalidInput(msg: String) extends DivisionError

def divideTyped(a: Int, b: Int): Either[DivisionError, Int] = {
  if (b == 0) Left(DivisionByZero)
  else Right(a / b)
}

Error propagation:

Clojure	Scala	Notes
try/catch	try/catch	For exceptions
{:ok/:error} maps	Either[L, R]	Typed error handling
Nil for absence	Option[A]	Safe nullability
ex-info with data	Custom case classes	Structured errors
Error threading	.map(), .flatMap()	Monadic composition

For-comprehension error handling:

def compute(a: Int, b: Int, c: Int): Either[String, Int] = {
  for {
    x <- divideEither(a, b)
    y <- divideEither(x, c)
    z <- divideEither(y, 2)
  } yield z
}

// Equivalent to nested flatMap/map
divideEither(a, b)
  .flatMap(x => divideEither(x, c))
  .flatMap(y => divideEither(y, 2))

---

Concurrency Patterns

STM (Software Transactional Memory)

Clojure:

(def account-a (ref 1000))
(def account-b (ref 2000))

(dosync
  (alter account-a - 100)
  (alter account-b + 100))

Scala:

// Scala STM (stm library)
import scala.concurrent.stm._

val accountA = Ref(1000)
val accountB = Ref(2000)

atomic { implicit txn =>
  accountA -= 100
  accountB += 100
}

// Or Cats Effect STM
import cats.effect.IO
import cats.effect.std.{Ref => CatsRef}

val program = for {
  accountA <- CatsRef[IO].of(1000)
  accountB <- CatsRef[IO].of(2000)
  _ <- (
    accountA.update(_ - 100),
    accountB.update(_ + 100)
  ).parTupled
} yield ()

Agents → Akka Actors

Clojure:

(def logger (agent []))

(send logger conj "Log entry 1")
(send logger conj "Log entry 2")

(await logger)
@logger  ;; => ["Log entry 1" "Log entry 2"]

Scala:

// Akka Typed Actors
import akka.actor.typed._
import akka.actor.typed.scaladsl.Behaviors

sealed trait LogMessage
case class AddEntry(entry: String) extends LogMessage
case class GetEntries(replyTo: ActorRef[List[String]]) extends LogMessage

def logger(entries: List[String]): Behavior[LogMessage] = {
  Behaviors.receive { (context, message) =>
    message match {
      case AddEntry(entry) =>
        logger(entries :+ entry)
      case GetEntries(replyTo) =>
        replyTo ! entries
        Behaviors.same
    }
  }
}

val system = ActorSystem(logger(List.empty), "logger")
system ! AddEntry("Log entry 1")
system ! AddEntry("Log entry 2")

Futures

Clojure:

(def result (future
              (Thread/sleep 1000)
              42))

@result  ;; Blocks until complete

Scala:

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

val result = Future {
  Thread.sleep(1000)
  42
}

// Await (blocking)
import scala.concurrent.Await
import scala.concurrent.duration._

Await.result(result, 2.seconds)

// Or use callbacks (non-blocking)
result.foreach(println)

// Or for-comprehension
val combined = for {
  a <- Future(10)
  b <- Future(20)
} yield a + b

---

Platform & Performance

JVM Optimization

Both Clojure and Scala run on JVM, but Scala can be more performant with proper type usage:

Performance considerations:

Aspect	Clojure	Scala	Notes
Boxing overhead	Higher (dynamic)	Lower (primitives)	Scala specialization reduces boxing
Method dispatch	Dynamic (slower)	Static or dynamic	Scala pattern matching is faster than multimethods
Collection operations	Generic	Specialized	Scala collections can be optimized per type
Lazy evaluation	Lazy sequences	Streams/LazyList	Both are lazy, similar performance
Type erasure	N/A (dynamic)	Yes (generics)	Both have JVM type erasure

Optimization patterns:

// Use specialized collections for primitives
val ints: Array[Int] = Array(1, 2, 3)  // No boxing
val doubles: Vector[Double] = Vector(1.0, 2.0)  // Specialized

// Use @specialized for generic code
def sum[@specialized(Int, Long, Double) A: Numeric](list: List[A]): A = {
  list.sum
}

// Use tail recursion
@scala.annotation.tailrec
def factorial(n: Int, acc: Int = 1): Int = {
  if (n <= 1) acc
  else factorial(n - 1, n * acc)
}

// Use views for lazy evaluation
val result = (1 to 1000000).view
  .map(_ * 2)
  .filter(_ > 100)
  .take(10)
  .toList  // Only 10 elements processed

---

Common Pitfalls

1. Treating Everything as Maps

   • Clojure: Maps everywhere
   • Scala mistake: Using Map[String, Any] instead of case classes
   • Fix: Use case classes for structured data, sealed traits for variants

2. Ignoring Static Types

   • Clojure: Dynamic typing
   • Scala mistake: Avoiding types with excessive Any
   • Fix: Embrace Scala's type system; let compiler help

3. Missing nil vs. None

   • Clojure: nil is pervasive and safe
   • Scala mistake: Using null instead of Option
   • Fix: Always use Option[A] for nullable values

4. Over-using Mutable Collections

   • Clojure: Immutable by default
   • Scala mistake: Using mutable collections from scala.collection.mutable
   • Fix: Prefer immutable collections; use mutable only for performance-critical code

5. Threading Macros → Complex Chains

   • Clojure: ->> for elegant pipelines
   • Scala mistake: Creating unreadable method chains
   • Fix: Break chains into intermediate vals; use for-comprehensions

6. Macros Everywhere

   • Clojure: Macros are common
   • Scala mistake: Trying to replicate with complex macros
   • Fix: Use higher-order functions, inline methods, or accept language differences

7. Keyword Keys → String Keys

   • Clojure: Keywords :key are optimized and fast
   • Scala mistake: Using strings for map keys in structured data
   • Fix: Use case classes or sealed traits for structured types

8. Lazy Sequences → Streams Without Forcing

   • Clojure: Lazy seqs can cause holding onto head
   • Scala: Similar with LazyList
   • Fix: Force realization when needed or use strict collections

9. Multimethod Dispatch → Pattern Matching

   • Clojure: Multimethods are open and extensible
   • Scala: Sealed traits are closed

---

Tooling

Tool	Purpose	Notes
sbt	Build tool	Standard Scala build tool
Mill	Build tool	Modern alternative to sbt
IntelliJ IDEA	IDE	Best Scala IDE with refactoring support
Metals	Language server	For VS Code, Emacs, Vim
Scalafmt	Formatter	Code formatting
Scalafix	Linter	Automated refactoring and linting
ScalaTest	Testing	Most popular test framework
ScalaCheck	Property testing	Like Clojure test.check
Wartremover	Linter	Detect unsafe patterns
Akka	Concurrency	Actor model for concurrency
Cats / Cats Effect	FP library	Functional programming abstractions
FS2	Streaming	Functional streams (replaces core.async)

---

Examples

Example 1: Simple - List Processing

Before (Clojure):

(def numbers [1 2 3 4 5])

(defn process [nums]
  (->> nums
       (filter even?)
       (map #(* % 2))
       (reduce +)))

(process numbers)  ;; => 12

After (Scala):

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

def process(nums: List[Int]): Int = {
  nums
    .filter(_ % 2 == 0)
    .map(_ * 2)
    .sum
}

process(numbers)  // => 12

Example 2: Medium - Error Handling with Either

Before (Clojure):

(defn parse-int [s]
  (try
    {:ok (Integer/parseInt s)}
    (catch NumberFormatException e
      {:error "Invalid number"})))

(defn divide [a b]
  (if (zero? b)
    {:error "Division by zero"}
    {:ok (/ a b)}))

(defn compute [a-str b-str]
  (let [a-result (parse-int a-str)]
    (if (:error a-result)
      a-result
      (let [b-result (parse-int b-str)]
        (if (:error b-result)
          b-result
          (let [a (:ok a-result)
                b (:ok b-result)]
            (divide a b)))))))

(compute "10" "2")  ;; => {:ok 5}
(compute "10" "0")  ;; => {:error "Division by zero"}
(compute "abc" "2") ;; => {:error "Invalid number"}

After (Scala):

def parseInt(s: String): Either[String, Int] = {
  try {
    Right(s.toInt)
  } catch {
    case _: NumberFormatException => Left("Invalid number")
  }
}

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

def compute(aStr: String, bStr: String): Either[String, Int] = {
  for {
    a <- parseInt(aStr)
    b <- parseInt(bStr)
    result <- divide(a, b)
  } yield result
}

compute("10", "2")   // => Right(5)
compute("10", "0")   // => Left("Division by zero")
compute("abc", "2")  // => Left("Invalid number")

Example 3: Complex - ADT with Pattern Matching

Before (Clojure):

;; Tagged map representation
(defn circle [radius]
  {:type :circle :radius radius})

(defn rectangle [width height]
  {:type :rectangle :width width :height height})

(defn triangle [base height]
  {:type :triangle :base base :height height})

;; Multimethod dispatch
(defmulti area :type)

(defmethod area :circle [{:keys [radius]}]
  (* Math/PI radius radius))

(defmethod area :rectangle [{:keys [width height]}]
  (* width height))

(defmethod area :triangle [{:keys [base height]}]
  (* 0.5 base height))

(defmulti perimeter :type)

(defmethod perimeter :circle [{:keys [radius]}]
  (* 2 Math/PI radius))

(defmethod perimeter :rectangle [{:keys [width height]}]
  (* 2 (+ width height)))

(defmethod perimeter :triangle [{:keys [base height]}]
  ;; Simplified - assuming right triangle
  (+ base height (Math/sqrt (+ (* base base) (* height height)))))

;; Usage
(def shapes
  [(circle 5)
   (rectangle 4 6)
   (triangle 3 4)])

(defn total-area [shapes]
  (reduce + (map area shapes)))

(defn describe-shape [shape]
  (let [a (area shape)
        p (perimeter shape)]
    (str "Area: " (format "%.2f" a)
         ", Perimeter: " (format "%.2f" p))))

;; Results
(total-area shapes)  ;; => ~113.54
(map describe-shape shapes)
;; => ("Area: 78.54, Perimeter: 31.42"
;;     "Area: 24.00, Perimeter: 20.00"
;;     "Area: 6.00, Perimeter: 12.00")

After (Scala):

sealed trait Shape {
  def area: Double
  def perimeter: Double
}

case class Circle(radius: Double) extends Shape {
  def area: Double = math.Pi * radius * radius
  def perimeter: Double = 2 * math.Pi * radius
}

case class Rectangle(width: Double, height: Double) extends Shape {
  def area: Double = width * height
  def perimeter: Double = 2 * (width + height)
}

case class Triangle(base: Double, height: Double) extends Shape {
  def area: Double = 0.5 * base * height
  def perimeter: Double = {
    // Simplified - assuming right triangle
    base + height + math.sqrt(base * base + height * height)
  }
}

// Usage
val shapes = List(
  Circle(5),
  Rectangle(4, 6),
  Triangle(3, 4)
)

def totalArea(shapes: List[Shape]): Double = {
  shapes.map(_.area).sum
}

def describeShape(shape: Shape): String = {
  val a = shape.area
  val p = shape.perimeter
  f"Area: $a%.2f, Perimeter: $p%.2f"
}

// Results
totalArea(shapes)  // => 113.53981633974483
shapes.map(describeShape)
// => List(
//      "Area: 78.54, Perimeter: 31.42",
//      "Area: 24.00, Perimeter: 20.00",
//      "Area: 6.00, Perimeter: 12.00"
//    )

// Pattern matching variant
def describeShapeMatch(shape: Shape): String = shape match {
  case Circle(r) =>
    s"Circle with radius $r"
  case Rectangle(w, h) =>
    s"Rectangle ${w}x$h"
  case Triangle(b, h) =>
    s"Triangle with base $b and height $h"
}

---

See Also

For more examples and patterns, see:

• meta-convert-dev - Foundational patterns with cross-language examples
• convert-python-scala - Similar dynamic → static conversion
• convert-typescript-scala - Another typed target language
• lang-clojure-dev - Clojure development patterns
• lang-scala-dev - Scala development patterns

Cross-cutting pattern skills:

• patterns-concurrency-dev - Async, channels, threads across languages
• patterns-serialization-dev - JSON, validation across languages
• patterns-metaprogramming-dev - Macros, compile-time code generation