Convert Clojure Haskell

Clojure ↔ Haskell Conversion

Bidirectional conversion between Clojure and Haskell. This skill extends meta-convert-dev with Clojure↔Haskell specific type mappings, idiom translations, and tooling for migrating functional JVM code to native compiled Haskell.

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 types → Haskell types
• Idiom translations: Clojure patterns → idiomatic Haskell
• Error handling: Clojure exceptions → Haskell Maybe/Either
• Concurrency patterns: Clojure atoms/refs/agents → Haskell STM/async
• REPL workflow: REPL-driven development → GHCi interactive development
• Macro translation: Clojure macros → Haskell Template Haskell or type-level patterns

This Skill Does NOT Cover

• General conversion methodology - see meta-convert-dev
• Clojure language fundamentals - see lang-clojure-dev
• Haskell language fundamentals - see lang-haskell-dev
• ClojureScript → PureScript/Elm - see dedicated frontend conversion skills

Quick Reference

When Converting Code

1. Analyze source thoroughly - Understand Clojure's dynamic nature before writing static Haskell
2. Map types first - Clojure's dynamic types need explicit Haskell types
3. Preserve semantics over syntax similarity
4. Embrace static typing - Use Haskell's type system to prevent runtime errors
5. Handle nil properly - All potential nils become Maybe/Either
6. Test equivalence - Same inputs → same outputs for pure logic

Type System Mapping

Primitive Types

Collection Types

Composite Types

Nil and Optional Values

Clojure:

(defn find-user [id users]
  (first (filter #(= (:id %) id) users)))  ; Returns nil if not found

(get {:name "Alice"} :age)  ; Returns nil

Haskell:

import Data.Maybe (listToMaybe)
import qualified Data.Map as Map

findUser :: Int -> [User] -> Maybe User
findUser userId = listToMaybe . filter (\u -> userId == userId u)

-- Map lookup returns Maybe
Map.lookup "age" (Map.fromList [("name", "Alice")])  -- Nothing

Why this translation:

• Clojure's nil is pervasive; Haskell makes optionality explicit with Maybe
• All nil checks in Clojure become pattern matches on Nothing/Just
• Type safety prevents null pointer exceptions at compile time

Idiom Translation

Pattern 1: Threading Macros to Function Composition

Clojure:

;; Thread-first (->)
(-> data
    (parse-json)
    (get :users)
    (filter active?)
    (map :name)
    (sort))

;; Thread-last (->>)
(->> (range 100)
     (map inc)
     (filter even?)
     (reduce +))

Haskell:

import Data.Function ((&))
import qualified Data.List as List

-- Using function composition (right to left)
processData :: Value -> [Text]
processData = List.sort . map name . filter active . getUsers . parseJSON

-- Or using & operator (left to right, like ->)
processData' :: Value -> [Text]
processData' data =
    data
    & parseJSON
    & getUsers
    & filter active
    & map name
    & List.sort

-- Thread-last style
sumEvenInc :: Int
sumEvenInc = sum . filter even . map (+1) $ [0..99]

-- Or with $
sumEvenInc' = sum $ filter even $ map (+1) [0..99]

Why this translation:

• Clojure's -> maps to Haskell's & operator (from Data.Function)
• Function composition (.) is more idiomatic in Haskell but reads right-to-left
• Use $ for right-associative application
• Pattern: (->> x f g h) → h . g . f $ x

Pattern 2: Destructuring

Clojure:

;; Sequential destructuring
(let [[a b & rest] [1 2 3 4 5]]
  (+ a b))

;; Map destructuring
(defn greet [{:keys [name age] :or {age 0}}]
  (str "Hello " name ", age " age))

(greet {:name "Alice" :age 30})

Haskell:

-- Pattern matching on lists
exampleList :: [Int] -> Int
exampleList (a:b:rest) = a + b
exampleList _ = 0

-- Record pattern matching
data Person = Person { name :: Text, age :: Int }

greet :: Person -> Text
greet Person{name, age} = "Hello " <> name <> ", age " <> show age

-- With default values (Maybe pattern)
greetMaybe :: Maybe Int -> Person -> Text
greetMaybe maybeAge Person{name} =
    let actualAge = fromMaybe 0 maybeAge
    in "Hello " <> name <> ", age " <> show actualAge

Why this translation:

• Clojure's destructuring is runtime; Haskell's pattern matching is compile-time
• Haskell enforces exhaustive pattern matching
• Record syntax provides named field access
• Default values use Maybe or function parameters

Pattern 3: Sequence Operations

Clojure:

;; Map, filter, reduce
(->> users
     (filter :active)
     (map :email)
     (filter valid-email?)
     (reduce conj #{}))

;; List comprehension with for
(for [x (range 10)
      y (range 10)
      :when (= (+ x y) 10)]
  [x y])

Haskell:

import Data.Set (Set)
import qualified Data.Set as Set

-- Map, filter, fold
processUsers :: [User] -> Set Email
processUsers =
    Set.fromList .
    filter validEmail .
    map email .
    filter active

-- List comprehension
pairs :: [(Int, Int)]
pairs = [(x, y) | x <- [0..9], y <- [0..9], x + y == 10]

-- Or with do-notation (list monad)
pairs' :: [(Int, Int)]
pairs' = do
    x <- [0..9]
    y <- [0..9]
    guard (x + y == 10)
    return (x, y)

Why this translation:

• Both languages support functional pipelines
• Haskell's list comprehensions are more powerful (guards, multiple generators)
• Set.fromList is idiomatic for building sets from lists
• Do-notation provides monadic abstraction over list comprehension

Pattern 4: Lazy Sequences

Clojure:

;; Infinite sequences
(def naturals (iterate inc 0))
(take 5 naturals)  ; (0 1 2 3 4)

;; Lazy evaluation
(def evens (filter even? naturals))
(take 3 evens)  ; (0 2 4)

;; Custom lazy sequence
(defn fibonacci []
  (map first (iterate (fn [[a b]] [b (+ a b)]) [0 1])))

Haskell:

-- Infinite lists (lazy by default)
naturals :: [Integer]
naturals = iterate (+1) 0

take 5 naturals  -- [0,1,2,3,4]

-- Lazy filtering
evens :: [Integer]
evens = filter even naturals

take 3 evens  -- [0,2,4]

-- Custom lazy sequence
fibonacci :: [Integer]
fibonacci = 0 : 1 : zipWith (+) fibonacci (tail fibonacci)

-- Or more explicit
fibonacci' :: [Integer]
fibonacci' = map fst $ iterate (\(a, b) -> (b, a + b)) (0, 1)

Why this translation:

• Both languages are lazy by default for sequences
• Haskell's laziness is pervasive; Clojure's is opt-in for sequences
• Infinite data structures work the same way
• Haskell's zipWith provides elegant recursive definitions

Paradigm Translation

Mental Model: Dynamic → Static Typing

Concurrency Mental Model

Error Handling

Exceptions → Maybe/Either

Clojure:

(defn parse-age [s]
  (try
    (let [age (Integer/parseInt s)]
      (if (pos? age)
        age
        (throw (ex-info "Age must be positive" {:age age}))))
    (catch NumberFormatException e
      (throw (ex-info "Invalid number" {:input s})))))

(defn validate-user [age-str email-str]
  (try
    {:age (parse-age age-str)
     :email (validate-email email-str)}
    (catch Exception e
      nil)))

Haskell:

import Text.Read (readMaybe)
import Data.Text (Text)

data ValidationError
    = InvalidNumber Text
    | NegativeAge Int
    | InvalidEmail Text
    deriving (Show, Eq)

parseAge :: Text -> Either ValidationError Int
parseAge s =
    case readMaybe (unpack s) of
        Nothing -> Left (InvalidNumber s)
        Just age ->
            if age > 0
                then Right age
                else Left (NegativeAge age)

validateUser :: Text -> Text -> Either ValidationError User
validateUser ageStr emailStr = do
    age <- parseAge ageStr
    email <- validateEmail emailStr
    return $ User age email

-- Or with Applicative for independent validations
validateUser' :: Text -> Text -> Either ValidationError User
validateUser' ageStr emailStr =
    User <$> parseAge ageStr <*> validateEmail emailStr

Why this translation:

• Clojure exceptions are runtime; Haskell Either is type-checked
• Either forces handling of error cases at compile time
• Do-notation provides clean error chaining (like try/catch flow)
• Applicative style validates independently and collects errors

Exception Handling Patterns

Concurrency Patterns

Atoms → IORef/TVar

Clojure:

(def counter (atom 0))

;; Atomic update
(swap! counter inc)
(swap! counter + 10)

;; Read value
@counter

;; Reset value
(reset! counter 0)

;; Conditional update
(compare-and-set! counter 0 100)

Haskell:

import Data.IORef
import Control.Concurrent.STM

-- Using IORef (not transactional)
example :: IO ()
example = do
    counter <- newIORef 0

    -- Atomic update
    modifyIORef' counter (+1)
    modifyIORef' counter (+10)

    -- Read value
    value <- readIORef counter

    -- Write value
    writeIORef counter 0

-- Using TVar (transactional)
exampleSTM :: IO ()
exampleSTM = do
    counter <- newTVarIO 0

    atomically $ do
        modifyTVar' counter (+1)
        modifyTVar' counter (+10)

    -- Read
    value <- readTVarIO counter

    -- Write
    atomically $ writeTVar counter 0

Why this translation:

• Clojure atoms provide atomic updates; Haskell IORef or TVar similar
• For simple cases, IORef sufficient; for composition, use STM
• STM provides composable transactions like Clojure refs
• Both guarantee atomic updates without locks

Refs → Software Transactional Memory (STM)

Clojure:

(def account-a (ref 100))
(def account-b (ref 200))

;; Coordinated transaction
(dosync
  (alter account-a - 50)
  (alter account-b + 50))

;; Read consistent snapshot
(dosync
  [@account-a @account-b])

Haskell:

import Control.Concurrent.STM

transfer :: TVar Int -> TVar Int -> Int -> IO ()
transfer fromAccount toAccount amount = atomically $ do
    fromBalance <- readTVar fromAccount
    toBalance <- readTVar toAccount
    writeTVar fromAccount (fromBalance - amount)
    writeTVar toAccount (toBalance + amount)

-- Read consistent snapshot
readAccounts :: TVar Int -> TVar Int -> IO (Int, Int)
readAccounts account1 account2 = atomically $ do
    bal1 <- readTVar account1
    bal2 <- readTVar account2
    return (bal1, bal2)

-- Usage
main :: IO ()
main = do
    accountA <- newTVarIO 100
    accountB <- newTVarIO 200
    transfer accountA accountB 50
    (a, b) <- readAccounts accountA accountB
    print (a, b)  -- (50, 250)

Why this translation:

• Both use Software Transactional Memory for coordinated updates
• Clojure's dosync = Haskell's atomically
• alter/commute = modifyTVar/writeTVar
• Both provide automatic retry on conflicts
• Both guarantee ACID properties

Agents → Async

Clojure:

(def logger (agent []))

;; Send async update
(send logger conj "Entry 1")
(send logger conj "Entry 2")

;; Wait for completion
(await logger)

;; For blocking operations
(send-off logger
  (fn [logs]
    (Thread/sleep 1000)
    (conj logs "Delayed")))

Haskell:

import Control.Concurrent.Async
import Control.Concurrent (threadDelay)

-- Using async library
exampleAsync :: IO ()
exampleAsync = do
    -- Launch async computations
    a1 <- async $ return (1 :: Int)
    a2 <- async $ return (2 :: Int)

    -- Wait for results
    result1 <- wait a1
    result2 <- wait a2

    print (result1 + result2)

-- For sequential async operations (like agents)
processLogs :: [String] -> IO ()
processLogs initialLogs = do
    ref <- newIORef initialLogs

    -- Spawn background worker
    async $ do
        threadDelay 1000000  -- 1 second
        modifyIORef' ref (++ ["Delayed entry"])

    -- Continue main work...
    return ()

Why this translation:

• Clojure agents are for async sequential updates
• Haskell async provides concurrent execution
• For sequential updates, combine async with IORef
• Both allow non-blocking computation

core.async → Channels

Clojure:

(require '[clojure.core.async :as async])

(let [ch (async/chan 10)]
  ;; Producer
  (async/go
    (async/>! ch "Hello")
    (async/>! ch "World")
    (async/close! ch))

  ;; Consumer
  (async/go-loop []
    (when-let [msg (async/<! ch)]
      (println msg)
      (recur))))

Haskell:

import Control.Concurrent
import Control.Concurrent.Chan

exampleChannels :: IO ()
exampleChannels = do
    ch <- newChan

    -- Producer
    forkIO $ do
        writeChan ch "Hello"
        writeChan ch "World"
        -- Note: Chan doesn't have explicit close

    -- Consumer
    forkIO $ forever $ do
        msg <- readChan ch
        putStrLn msg

    threadDelay 1000000  -- Wait for processing

-- Or with STM channels (bounded)
import Control.Concurrent.STM.TBQueue

exampleBounded :: IO ()
exampleBounded = do
    queue <- newTBQueueIO 10

    forkIO $ do
        atomically $ writeTBQueue queue "Hello"
        atomically $ writeTBQueue queue "World"

    forkIO $ forever $ do
        msg <- atomically $ readTBQueue queue
        putStrLn msg

Why this translation:

• Both provide CSP-style channels for communication
• Clojure's core.async go blocks = Haskell's forkIO
• STM channels provide bounded queues like core.async
• Both enable producer/consumer patterns

Memory & Ownership

Immutability by Default

Both Clojure and Haskell embrace immutability, but with different enforcement:

Clojure:

;; All updates return new values
(def v1 [1 2 3])
(def v2 (conj v1 4))  ; v1 unchanged
;; v1 => [1 2 3]
;; v2 => [1 2 3 4]

;; Structural sharing
(def big-map (into {} (map vector (range 10000) (range 10000))))
(def updated (assoc big-map 5000 "changed"))  ; O(log n), shares most nodes

Haskell:

-- All values are immutable by default
v1 = [1, 2, 3]
v2 = v1 ++ [4]  -- v1 unchanged
-- v1 = [1,2,3]
-- v2 = [1,2,3,4]

-- Structural sharing via laziness
import qualified Data.Map as Map

bigMap = Map.fromList [(i, i) | i <- [0..9999]]
updated = Map.insert 5000 "changed" bigMap  -- O(log n), shares structure

Why this translation:

• Both languages default to immutability
• Haskell enforces purity via types; Clojure via convention
• Performance characteristics similar due to structural sharing
• Mutable state explicit in both (atoms/refs vs IORef/TVar)

Macro Translation

Clojure Macros → Haskell Alternatives

Clojure macros operate at the syntactic level; Haskell provides multiple alternatives:

Clojure:

;; Custom control flow macro
(defmacro unless [condition & body]
  `(if (not ~condition)
     (do ~@body)))

(unless false
  (println "This runs"))

;; DSL macro
(defmacro with-logging [expr]
  `(let [start# (System/currentTimeMillis)
         result# ~expr]
     (println "Took" (- (System/currentTimeMillis) start#) "ms")
     result#))

Haskell:

{-# LANGUAGE TemplateHaskell #-}

import Language.Haskell.TH

-- Template Haskell for code generation
-- (Advanced use case, often unnecessary)

-- More idiomatic: Higher-order functions
unless :: Bool -> IO () -> IO ()
unless condition action =
    if not condition
        then action
        else return ()

-- Usage
unless False $ putStrLn "This runs"

-- Logging via function composition
import System.CPUTime
import Text.Printf

withLogging :: IO a -> IO a
withLogging action = do
    start <- getCPUTime
    result <- action
    end <- getCPUTime
    let diff = fromIntegral (end - start) / (10^12)
    printf "Computation time: %0.3f sec\n" (diff :: Double)
    return result

-- Usage
withLogging $ do
    putStrLn "Working..."
    return ()

Why this translation:

• Most Clojure macros can be replaced with Haskell functions
• Higher-order functions provide abstraction without compile-time magic
• Template Haskell available for true compile-time metaprogramming
• Type system + operators enable many DSLs without macros

Common Pitfalls

1. Dynamic Type Assumptions → Static Type Requirements

Problem: Clojure allows heterogeneous collections; Haskell requires homogeneous types.

;; Clojure: Mixed types OK
(def mixed [1 "two" :three 4.0])

-- Haskell: Need sum type for mixed
data Value
    = IntVal Int
    | StringVal String
    | KeywordVal String
    | DoubleVal Double

mixed :: [Value]
mixed = [IntVal 1, StringVal "two", KeywordVal "three", DoubleVal 4.0]

Fix: Use algebraic data types (ADTs) to represent variants.

2. Nil Propagation → Maybe Chaining

Problem: Clojure's nil freely propagates; Haskell requires explicit handling.

;; Clojure: nil just flows through
(-> data :user :email str/upper-case)  ; NPE if any step is nil

-- Haskell: Must handle Maybe at each step
import qualified Data.Text as T
import Data.Maybe (fromMaybe)

processEmail :: Data -> Maybe T.Text
processEmail d = do
    user <- getUser d
    email <- getEmail user
    return $ T.toUpper email

-- Or with combinators
processEmail' :: Data -> T.Text
processEmail' d =
    fromMaybe "" $ fmap T.toUpper (getUser d >>= getEmail)

Fix: Use Maybe monad or applicative functors to chain computations.

3. Lazy Sequences vs Lazy Evaluation

Problem: Clojure sequences are explicitly lazy; Haskell is lazy everywhere.

;; Clojure: Force evaluation when needed
(let [xs (map expensive-fn (range 1000))]
  (doall xs)  ; Force evaluation
  xs)

-- Haskell: Lazy by default, force with strictness annotations
import Control.DeepSeq

let xs = map expensiveFn [0..999]
in xs `deepseq` xs  -- Force full evaluation

-- Or use strict data structures
import qualified Data.Vector as V

let xs = V.map expensiveFn (V.enumFromN 0 1000)
in xs  -- Vector is strict

Fix: Understand laziness difference; use strict evaluation when needed.

4. Keyword Keys → Text or Custom Types

Problem: Clojure keywords don't have direct Haskell equivalent.

;; Clojure: Keywords as map keys
{:name "Alice" :age 30 :email "alice@example.com"}

-- Option 1: Use records (preferred)
data User = User
    { name :: Text
    , age :: Int
    , email :: Text
    }

-- Option 2: Use Text keys in Map
import qualified Data.Map as Map

userMap :: Map Text String
userMap = Map.fromList
    [ ("name", "Alice")
    , ("age", "30")
    , ("email", "alice@example.com")
    ]

-- Option 3: Custom keyword type
newtype Keyword = Keyword Text deriving (Eq, Ord, Show)

keywordMap :: Map Keyword String
keywordMap = Map.fromList
    [ (Keyword "name", "Alice")
    , (Keyword "age", "30")
    ]

Fix: Use records for structured data; Map for truly dynamic cases.

5. REPL Workflow Differences

Problem: Clojure's REPL allows redefining anything; GHCi more restricted.

Clojure:

;; Can reload everything at runtime
(require 'myapp.core :reload)
(in-ns 'myapp.core)

Haskell (GHCi):

-- Type changes require restart
:reload  -- Reload current modules
:type expr  -- Check types
:info Name  -- Get information

-- For rapid development, use ghcid
-- ghcid watches files and reloads automatically

Fix: Use ghcid for auto-reload; accept that type changes need restart.

Tooling

Development Tools

Build Configuration Mapping

Clojure (project.clj):

(defproject myapp "0.1.0"
  :dependencies [[org.clojure/clojure "1.11.1"]
                 [cheshire "5.12.0"]
                 [compojure "1.7.0"]]
  :main myapp.core)

Haskell (package.yaml or .cabal):

# package.yaml (for stack/hpack)
name: myapp
version: 0.1.0

dependencies:
  - base >= 4.7 && < 5
  - aeson  # JSON (like cheshire)
  - text   # Text handling
  - warp   # Web server (like ring)

executables:
  myapp:
    main: Main.hs
    source-dirs: src

Library Equivalents

Examples

Example 1: Simple - Data Transformation

Before (Clojure):

(defn process-users [users]
  (->> users
       (filter :active)
       (map :email)
       (map str/lower-case)
       (into #{})))

;; Usage
(process-users
  [{:name "Alice" :email "ALICE@EXAMPLE.COM" :active true}
   {:name "Bob" :email "BOB@EXAMPLE.COM" :active false}
   {:name "Carol" :email "CAROL@EXAMPLE.COM" :active true}])
;; => #{"alice@example.com" "carol@example.com"}

After (Haskell):

import qualified Data.Text as T
import qualified Data.Set as Set

data User = User
    { name :: T.Text
    , email :: T.Text
    , active :: Bool
    } deriving (Show, Eq)

processUsers :: [User] -> Set.Set T.Text
processUsers =
    Set.fromList .
    map (T.toLower . email) .
    filter active

-- Usage
let users =
        [ User "Alice" "ALICE@EXAMPLE.COM" True
        , User "Bob" "BOB@EXAMPLE.COM" False
        , User "Carol" "CAROL@EXAMPLE.COM" True
        ]
in processUsers users
-- fromList ["alice@example.com","carol@example.com"]

Example 2: Medium - Error Handling

Before (Clojure):

(defn parse-user [data]
  (try
    (let [age (Integer/parseInt (:age data))]
      (when (neg? age)
        (throw (ex-info "Negative age" {:age age})))
      {:name (:name data)
       :age age
       :email (:email data)})
    (catch NumberFormatException e
      (throw (ex-info "Invalid age format" {:input (:age data)})))
    (catch Exception e
      nil)))

(defn process-user-data [raw-data]
  (try
    (let [user (parse-user raw-data)]
      (when (some nil? (vals user))
        (throw (ex-info "Missing fields" {:user user})))
      user)
    (catch Exception e
      {:error (.getMessage e)})))

After (Haskell):

import qualified Data.Text as T
import Text.Read (readMaybe)

data User = User
    { userName :: T.Text
    , userAge :: Int
    , userEmail :: T.Text
    } deriving (Show, Eq)

data ParseError
    = InvalidAgeFormat T.Text
    | NegativeAge Int
    | MissingField T.Text
    deriving (Show, Eq)

parseUser :: Map T.Text T.Text -> Either ParseError User
parseUser dataMap = do
    name <- maybe (Left $ MissingField "name") Right $ Map.lookup "name" dataMap
    ageStr <- maybe (Left $ MissingField "age") Right $ Map.lookup "age" dataMap
    email <- maybe (Left $ MissingField "email") Right $ Map.lookup "email" dataMap

    age <- case readMaybe (T.unpack ageStr) of
        Nothing -> Left $ InvalidAgeFormat ageStr
        Just a | a < 0 -> Left $ NegativeAge a
               | otherwise -> Right a

    return $ User name age email

-- Or with Applicative for cleaner code
import Control.Applicative ((<|>))

parseUser' :: Map T.Text T.Text -> Either ParseError User
parseUser' m =
    User <$> getField "name" m
         <*> (getField "age" m >>= parseAge)
         <*> getField "email" m
  where
    getField k = maybe (Left $ MissingField k) Right $ Map.lookup k m
    parseAge s = case readMaybe (T.unpack s) of
        Nothing -> Left $ InvalidAgeFormat s
        Just a | a < 0 -> Left $ NegativeAge a
               | otherwise -> Right a

Example 3: Complex - Concurrent Processing

Before (Clojure):

(require '[clojure.core.async :as async])

(defn fetch-user [id]
  (Thread/sleep 100)  ; Simulate network call
  {:id id :name (str "User-" id) :score (rand-int 100)})

(defn process-users [ids]
  (let [ch (async/chan)
        results (atom [])]
    ;; Spawn workers
    (doseq [id ids]
      (async/go
        (let [user (fetch-user id)]
          (async/>! ch user))))

    ;; Collect results
    (async/go-loop [remaining (count ids)]
      (when (pos? remaining)
        (let [user (async/<! ch)]
          (swap! results conj user)
          (recur (dec remaining)))))

    ;; Wait and return
    (Thread/sleep 500)
    (->> @results
         (sort-by :score)
         (reverse)
         (take 5))))

;; Usage
(process-users (range 20))

After (Haskell):

import Control.Concurrent.Async
import Control.Concurrent (threadDelay)
import Data.List (sortBy)
import Data.Ord (Down(..), comparing)

data User = User
    { userId :: Int
    , userName :: String
    , userScore :: Int
    } deriving (Show, Eq)

fetchUser :: Int -> IO User
fetchUser uid = do
    threadDelay 100000  -- 0.1 seconds (microseconds)
    score <- randomRIO (0, 99)
    return $ User uid ("User-" ++ show uid) score

processUsers :: [Int] -> IO [User]
processUsers ids = do
    -- Spawn async tasks
    asyncUsers <- mapM (async . fetchUser) ids

    -- Wait for all results
    users <- mapM wait asyncUsers

    -- Sort by score (descending) and take top 5
    let topUsers = take 5 $ sortBy (comparing (Down . userScore)) users

    return topUsers

-- Usage
main :: IO ()
main = do
    topUsers <- processUsers [0..19]
    mapM_ print topUsers

-- Alternative with parallel processing
import Control.Parallel.Strategies

processUsersParallel :: [Int] -> IO [User]
processUsersParallel ids = do
    users <- mapM fetchUser ids
    let sorted = take 5 $ sortBy (comparing (Down . userScore)) users
    return sorted

Why this translation:

• Clojure's core.async go blocks → Haskell's async tasks
• Both provide concurrent execution
• Haskell's async library handles errors automatically
• Sorting and taking top N is identical pattern
• Type safety prevents many concurrency bugs at compile time

See Also

For more examples and patterns, see:

• meta-convert-dev - Foundational patterns with cross-language examples
• convert-elm-haskell - Similar functional → Haskell conversion
• lang-clojure-dev - Clojure development patterns
• lang-haskell-dev - Haskell 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, Template Haskell across languages