Convert Clojure Elixir

Clojure ↔ Elixir Conversion

Bidirectional conversion between Clojure and Elixir. This skill extends meta-convert-dev with Clojure↔Elixir specific type mappings, idiom translations, and tooling for translating between these two functional languages across different runtimes (JVM → BEAM).

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 → Elixir types across runtimes
• Idiom translations: Lisp patterns → idiomatic Elixir
• Concurrency models: JVM threads/atoms → BEAM processes/GenServers
• Data structures: Persistent collections → Elixir's immutable structures
• Metaprogramming: Clojure macros → Elixir macros
• Build tools: Leiningen/deps.edn → Mix project structure
• REPL workflow: Clojure REPL → IEx patterns

This Skill Does NOT Cover

• General conversion methodology - see meta-convert-dev
• Clojure language fundamentals - see lang-clojure-dev
• Elixir language fundamentals - see lang-elixir-dev
• ClojureScript specifics - see convert-clojurescript-* skills

Quick Reference

When Converting Code

1. Analyze source thoroughly - Understand Clojure's JVM dependencies and state model
2. Map types first - Create type equivalence table, note platform differences
3. Preserve semantics - Functional purity translates well, state management differs
4. Rethink concurrency - Atoms/refs/agents → processes/GenServers/Agents
5. Modernize for BEAM - Embrace OTP patterns for fault tolerance
6. Adopt Elixir idioms - Pattern matching, with statements, pipe operator
7. Test equivalence - Same inputs → same outputs, verify supervision if stateful

Type System Mapping

Primitive Types

Collection Types

Composite Types

Paradigm Translation

Mental Model Shift: Clojure/JVM → Elixir/BEAM

Concurrency Mental Model

Idiom Translation

Pattern: Namespace to Module

Clojure:

(ns myapp.core
  "Application core namespace."
  (:require [clojure.string :as str]
            [clojure.set :as set]
            [myapp.util :refer [helper]]))

(defn greet [name]
  (str "Hello, " name "!"))

(defn- private-fn []
  "Private function")

Elixir:

defmodule Myapp.Core do
  @moduledoc """
  Application core module.
  """

  alias String, as: Str
  alias MapSet, as: Set
  import Myapp.Util, only: [helper: 1]

  @doc "Greets a person"
  @spec greet(String.t()) :: String.t()
  def greet(name) do
    "Hello, #{name}!"
  end

  defp private_fn do
    "Private function"
  end
end

Why this translation:

• ns → defmodule with CamelCase
• :require → alias for module aliases
• :refer → import for specific functions
• defn → def, defn- → defp
• String interpolation: (str ...) → "#{...}"
• @moduledoc and @doc for documentation
• @spec for type specifications

Pattern: Function Definition and Multi-Arity

Clojure:

;; Multi-arity function
(defn greet
  ([] (greet "World"))
  ([name] (str "Hello, " name "!"))
  ([greeting name] (str greeting ", " name "!")))

;; Variadic function
(defn sum [& numbers]
  (reduce + 0 numbers))

;; Pre/post conditions
(defn divide [numerator denominator]
  {:pre [(not= denominator 0)]
   :post [(number? %)]}
  (/ numerator denominator))

Elixir:

# Multi-arity with default arguments
def greet(name \\ "World")
def greet(name), do: "Hello, #{name}!"

# Or separate clauses for different behavior
def greet(), do: greet("World")
def greet(name) when is_binary(name), do: "Hello, #{name}!"
def greet(greeting, name), do: "#{greeting}, #{name}!"

# Variadic (rest parameters)
def sum(numbers) when is_list(numbers) do
  Enum.reduce(numbers, 0, &+/2)
end

# Guards replace pre-conditions
def divide(numerator, denominator) when denominator != 0 do
  numerator / denominator
end
def divide(_numerator, 0), do: {:error, :division_by_zero}

Why this translation:

• Multi-arity: Elixir uses default arguments \\ or separate clauses
• Variadic: Elixir requires explicit list, no & rest syntax
• Pre-conditions: Use guards (when)
• Post-conditions: Use explicit pattern matching on return
• Return tagged tuples {:ok, val} or {:error, reason} for safety

Pattern: Threading Macros

Clojure:

;; Thread-first
(-> 5
    (+ 3)
    (* 2)
    (- 1))
;; => 15

;; Thread-last
(->> [1 2 3 4 5]
     (map inc)
     (filter even?)
     (reduce +))
;; => 12

;; as-> for flexible positioning
(as-> 0 $
  (inc $)
  (+ 3 $)
  (* 2 $))
;; => 8

Elixir:

# Pipe operator (thread-first style)
5
|> Kernel.+(3)
|> Kernel.*(2)
|> Kernel.-(1)
# => 15

# More idiomatic with functions
[1, 2, 3, 4, 5]
|> Enum.map(&(&1 + 1))
|> Enum.filter(&rem(&1, 2) == 0)
|> Enum.reduce(0, &+/2)
# => 12

# Elixir pipe always threads as first argument
# For flexibility, use intermediate variables
value = 0
value = value + 1
value = 3 + value
value = value * 2
# => 8

Why this translation:

• -> → |> (pipe operator)
• Elixir pipe threads as first argument only
• ->> (thread-last) has no direct equivalent; use intermediate bindings
• as-> flexibility requires explicit variable assignment
• Embrace Elixir's Enum/Stream modules over manual threading

Pattern: Destructuring

Clojure:

;; Vector destructuring
(let [[a b c] [1 2 3]]
  (+ a b c))

;; Map destructuring
(let [{:keys [name age]} {:name "Alice" :age 30}]
  (str name " is " age))

;; With defaults
(let [{:keys [name age] :or {age 0}} {:name "Bob"}]
  age)

;; Nested destructuring
(let [{{city :city} :address} {:address {:city "NYC"}}]
  city)

;; Function arguments
(defn greet-person [{:keys [name age]}]
  (str "Hello " name ", you are " age))

Elixir:

# List destructuring
[a, b, c] = [1, 2, 3]
a + b + c

# Map destructuring
%{name: name, age: age} = %{name: "Alice", age: 30}
"#{name} is #{age}"

# With defaults (via pattern matching)
def get_age(%{age: age}), do: age
def get_age(_), do: 0

# Nested destructuring
%{address: %{city: city}} = %{address: %{city: "NYC"}}
city

# Function arguments
def greet_person(%{name: name, age: age}) do
  "Hello #{name}, you are #{age}"
end

# With head/tail
[head | tail] = [1, 2, 3]
# head = 1, tail = [2, 3]

Why this translation:

• Both support pattern matching destructuring
• Clojure :keys → Elixir explicit key: var or same name %{name: name}
• Defaults: Clojure :or → Elixir multiple function clauses
• Similar nested destructuring syntax
• Elixir has [head | tail] for list cons pattern

Pattern: Sequence Operations

Clojure:

;; map, filter, reduce
(->> data
     (map parse-record)
     (filter valid?)
     (map transform)
     (reduce (fn [acc item] (merge acc item)) {}))

;; List comprehension
(for [x (range 10)
      :when (even? x)
      :let [y (* x x)]]
  [x y])

;; Lazy sequences
(def naturals (iterate inc 0))
(take 5 naturals)
;; => (0 1 2 3 4)

Elixir:

# map, filter, reduce with Enum
data
|> Enum.map(&parse_record/1)
|> Enum.filter(&valid?/1)
|> Enum.map(&transform/1)
|> Enum.reduce(%{}, fn item, acc -> Map.merge(acc, item) end)

# List comprehension
for x <- 0..9, rem(x, 2) == 0 do
  y = x * x
  {x, y}
end

# Lazy sequences with Stream
naturals = Stream.iterate(0, &(&1 + 1))
Enum.take(naturals, 5)
# => [0, 1, 2, 3, 4]

Why this translation:

• ->> → |> with Enum module
• Clojure lazy by default; Elixir use Stream for laziness
• List comprehension: for in Clojure → for in Elixir (similar!)
• :when → guard in comprehension
• :let → inline binding in comprehension body
• iterate → Stream.iterate

Pattern: Atoms and State Management

Clojure:

;; Atom (synchronous state)
(def counter (atom 0))

@counter  ; Read
(swap! counter inc)  ; Update
(reset! counter 0)   ; Set
(compare-and-set! counter 0 10)  ; CAS

;; Agent (asynchronous state)
(def logger (agent []))
(send logger conj "log entry")
(await logger)

;; Ref (coordinated state)
(def account-a (ref 100))
(def account-b (ref 200))

(dosync
  (alter account-a - 50)
  (alter account-b + 50))

Elixir:

# Agent (async state - similar to Clojure Agent!)
{:ok, counter} = Agent.start_link(fn -> 0 end)

Agent.get(counter, & &1)           # Read
Agent.update(counter, &(&1 + 1))   # Update
Agent.update(counter, fn _ -> 0 end)  # Reset

# GenServer (for more complex state, like Clojure Atom with transactions)
defmodule Counter do
  use GenServer

  def start_link(initial), do: GenServer.start_link(__MODULE__, initial, name: __MODULE__)
  def get, do: GenServer.call(__MODULE__, :get)
  def increment, do: GenServer.call(__MODULE__, :increment)

  @impl true
  def init(initial), do: {:ok, initial}

  @impl true
  def handle_call(:get, _from, state), do: {:reply, state, state}
  def handle_call(:increment, _from, state), do: {:reply, state + 1, state + 1}
end

# Coordinated state (dosync) → GenServer with batched operations
defmodule Bank do
  use GenServer

  def transfer(from, to, amount) do
    GenServer.call(__MODULE__, {:transfer, from, to, amount})
  end

  @impl true
  def handle_call({:transfer, from, to, amount}, _from, accounts) do
    accounts = accounts
    |> Map.update!(from, &(&1 - amount))
    |> Map.update!(to, &(&1 + amount))
    {:reply, :ok, accounts}
  end
end

Why this translation:

• Clojure Atom → Elixir Agent (very similar for simple async state)
• Clojure Atom (with swap!) → Elixir GenServer.call (for synchronous)
• Clojure Agent → Elixir Agent (nearly 1:1 mapping!)
• Clojure Ref + dosync → GenServer (serialize transactions in single process)
• BEAM processes provide isolation and fault tolerance beyond JVM atoms

Pattern: Error Handling

Clojure:

;; try/catch
(try
  (/ 1 0)
  (catch ArithmeticException e
    (println "Error:" (.getMessage e))
    nil)
  (finally
    (println "Cleanup")))

;; With ex-info
(try
  (when (invalid? data)
    (throw (ex-info "Invalid data"
                    {:data data :reason :validation})))
  (process data)
  (catch clojure.lang.ExceptionInfo e
    (let [{:keys [data reason]} (ex-data e)]
      (log/error "Failed:" reason))))

Elixir:

# try/rescue/after
try do
  1 / 0
rescue
  e in ArithmeticError ->
    IO.puts("Error: #{Exception.message(e)}")
    nil
after
  IO.puts("Cleanup")
end

# Preferred: Tagged tuples with case/with
def divide(a, b) when b != 0, do: {:ok, a / b}
def divide(_, 0), do: {:error, :division_by_zero}

case divide(10, 0) do
  {:ok, result} -> result
  {:error, reason} -> handle_error(reason)
end

# with statement for chaining
def process_user(params) do
  with {:ok, validated} <- validate(params),
       {:ok, user} <- create_user(validated),
       {:ok, _email} <- send_welcome(user) do
    {:ok, user}
  else
    {:error, reason} -> {:error, reason}
  end
end

# Custom exceptions
defmodule ValidationError do
  defexception [:message, :data, :reason]
end

raise ValidationError, message: "Invalid", data: data, reason: :validation

Why this translation:

• try/catch → try/rescue (similar syntax)
• Elixir prefers tagged tuples {:ok, val} / {:error, reason} over exceptions
• ex-info → custom exception with defexception
• with statement chains operations that return tagged tuples
• Pattern match on error tuples rather than catch

Concurrency Patterns

Clojure Concurrency → Elixir Concurrency

Example: Parallel Processing

Clojure:

;; Using futures
(let [results (doall (map #(future (process-item %)) items))]
  (map deref results))

;; Using pmap (parallel map)
(pmap process-item items)

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

(let [c (a/chan)]
  (a/go
    (while true
      (let [item (a/<! c)]
        (process item))))
  (a/>!! c item))

Elixir:

# Using Task.async
results = items
|> Enum.map(&Task.async(fn -> process_item(&1) end))
|> Enum.map(&Task.await/1)

# Using Task.async_stream (parallel map)
items
|> Task.async_stream(&process_item/1, max_concurrency: 10)
|> Enum.map(fn {:ok, result} -> result end)

# Using GenServer for stateful processing
defmodule Processor do
  use GenServer

  def start_link(_), do: GenServer.start_link(__MODULE__, [], name: __MODULE__)

  def process_item(item) do
    GenServer.cast(__MODULE__, {:process, item})
  end

  @impl true
  def init([]), do: {:ok, []}

  @impl true
  def handle_cast({:process, item}, state) do
    result = process(item)
    {:noreply, [result | state]}
  end
end

Why this translation:

• future → Task.async for one-off async work
• pmap → Task.async_stream for parallel mapping
• core.async → GenStage for backpressure-aware pipelines
• BEAM's lightweight processes enable massive concurrency

Metaprogramming

Macros: Clojure → Elixir

Clojure:

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

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

;; Macro with gensym
(defmacro with-logging [& body]
  `(let [start# (System/currentTimeMillis)]
     (let [result# (do ~@body)]
       (println "Took" (- (System/currentTimeMillis) start#) "ms")
       result#)))

Elixir:

# Macros
defmacro unless(condition, do: block) do
  quote do
    if !unquote(condition) do
      unquote(block)
    end
  end
end

unless false do
  IO.puts("This runs")
  "result"
end

# Macro with unique variable (hygiene)
defmacro with_logging(do: block) do
  quote do
    start = System.monotonic_time(:millisecond)
    result = unquote(block)
    IO.puts("Took #{System.monotonic_time(:millisecond) - start} ms")
    result
  end
end

Why this translation:

• Syntax quote: ` → quote do
• Unquote: ~ → unquote()
• Splice: ~@ → unquote() in list context
• Auto-gensym: name# → automatic hygiene in Elixir
• Elixir macros use AST (abstract syntax tree) manipulation

Build and Dependencies

Leiningen → Mix

Clojure (project.clj):

(defproject myapp "0.1.0"
  :description "My Clojure app"
  :dependencies [[org.clojure/clojure "1.11.1"]
                 [cheshire "5.12.0"]
                 [ring/ring-core "1.10.0"]]
  :main myapp.core
  :profiles {:dev {:dependencies [[midje "1.10.9"]]}})

Elixir (mix.exs):

defmodule Myapp.MixProject do
  use Mix.Project

  def project do
    [
      app: :myapp,
      version: "0.1.0",
      elixir: "~> 1.14",
      start_permanent: Mix.env() == :prod,
      deps: deps()
    ]
  end

  def application do
    [
      extra_applications: [:logger],
      mod: {Myapp.Application, []}
    ]
  end

  defp deps do
    [
      {:jason, "~> 1.4"},         # Cheshire equivalent
      {:plug_cowboy, "~> 2.6"},   # Ring equivalent
      {:midje, "~> 1.10", only: :test}
    ]
  end
end

Common Commands

Testing

clojure.test → ExUnit

Clojure:

(ns myapp.core-test
  (:require [clojure.test :refer [deftest testing is are]]
            [myapp.core :as core]))

(deftest add-test
  (is (= 4 (core/add 2 2)))
  (is (= 0 (core/add -1 1))))

(deftest arithmetic-test
  (are [x y expected] (= expected (core/add x y))
    1 1 2
    2 3 5
    -1 1 0))

Elixir:

defmodule Myapp.CoreTest do
  use ExUnit.Case

  test "add/2 adds two numbers" do
    assert Myapp.Core.add(2, 2) == 4
    assert Myapp.Core.add(-1, 1) == 0
  end

  # Table-driven test (similar to are)
  test "arithmetic operations" do
    assert Myapp.Core.add(1, 1) == 2
    assert Myapp.Core.add(2, 3) == 5
    assert Myapp.Core.add(-1, 1) == 0
  end
end

Why this translation:

• deftest → test
• is → assert
• are → multiple assertions in test (no direct macro)
• ExUnit has powerful async testing: use ExUnit.Case, async: true
• Doctests: @doc examples become tests

Common Pitfalls

1. Vector vs List Performance

   • Clojure vectors: O(1) indexed access
   • Elixir lists: O(n) indexed access
   • Fix: Use tuples for small indexed collections, maps for key access

2. Keyword vs Atom Semantics

   • Clojure keywords are functions: (:name user)
   • Elixir atoms are not callable: user[:name] or user.name
   • Fix: Use map access syntax, not function application

3. Lazy by Default vs Eager

   • Clojure sequences lazy by default
   • Elixir Enum eager, Stream lazy
   • Fix: Explicitly use Stream for lazy operations

4. Shared State Model

   • Clojure: atoms/refs for shared state (thread-safe)
   • Elixir: processes for state (isolated)
   • Fix: Rethink state as processes, not shared memory

5. Nil Punning

   • Clojure: nil and false are falsy
   • Elixir: only false and nil are falsy (same!)
   • Fix: Actually works similarly, but be explicit in guards

6. Java Interop Loss

   • Clojure can call any Java library
   • Elixir runs on BEAM, not JVM
   • Fix: Find Elixir/Erlang equivalents or use ports/NIFs

7. Dynamic Vars

   • Clojure: dynamic binding with ^:dynamic and binding
   • Elixir: no dynamic binding; use process dictionary (discouraged) or explicit passing
   • Fix: Pass context explicitly or use Application environment

8. Metadata

   • Clojure: extensive metadata on symbols ^{:key val}
   • Elixir: module attributes @key val (compile-time only)
   • Fix: Use module attributes or store in data structures

Limitations

This skill focuses on core language translation. For complete migration, also consider:

• Java library dependencies: Must find Elixir/Erlang equivalents
• JVM-specific features: Reflection, classloaders, Java interop
• ClojureScript: Separate consideration for JavaScript target
• Runtime differences: JVM GC vs BEAM preemptive scheduling

Tooling

Examples

Example 1: Simple - Data Transformation

Before (Clojure):

(defn process-users [users]
  (->> users
       (filter :active)
       (map #(select-keys % [:id :name :email]))
       (map #(update % :name clojure.string/upper-case))))

After (Elixir):

def process_users(users) do
  users
  |> Enum.filter(& &1.active)
  |> Enum.map(&Map.take(&1, [:id, :name, :email]))
  |> Enum.map(&update_in(&1, [:name], fn n -> String.upcase(n) end))
end

Example 2: Medium - Stateful Counter

Before (Clojure):

(def counter (atom 0))

(defn increment []
  (swap! counter inc))

(defn get-count []
  @counter)

(defn reset-count []
  (reset! counter 0))

After (Elixir):

defmodule Counter do
  use Agent

  def start_link(initial_value) do
    Agent.start_link(fn -> initial_value end, name: __MODULE__)
  end

  def increment do
    Agent.update(__MODULE__, &(&1 + 1))
  end

  def get_count do
    Agent.get(__MODULE__, & &1)
  end

  def reset_count do
    Agent.update(__MODULE__, fn _ -> 0 end)
  end
end

Example 3: Complex - Concurrent Pipeline

Before (Clojure):

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

(defn process-pipeline [items]
  (let [input (a/chan 100)
        output (a/chan 100)]
    ;; Stage 1: Parse
    (a/go-loop []
      (when-let [item (a/<! input)]
        (let [parsed (parse-item item)]
          (a/>! output parsed))
        (recur)))

    ;; Stage 2: Validate
    (a/go-loop []
      (when-let [item (a/<! output)]
        (when (valid? item)
          (process item))
        (recur)))

    ;; Feed items
    (doseq [item items]
      (a/>!! input item))

    (a/close! input)))

After (Elixir):

defmodule Pipeline do
  def process_pipeline(items) do
    items
    |> Task.async_stream(&parse_item/1, max_concurrency: 10)
    |> Stream.filter(fn {:ok, item} -> valid?(item) end)
    |> Stream.map(fn {:ok, item} -> item end)
    |> Task.async_stream(&process/1, max_concurrency: 10)
    |> Stream.run()
  end

  defp parse_item(item), do: # ... parsing logic
  defp valid?(item), do: # ... validation logic
  defp process(item), do: # ... processing logic
end

# Or using GenStage for more control
defmodule Pipeline.Producer do
  use GenStage

  def start_link(items) do
    GenStage.start_link(__MODULE__, items)
  end

  def init(items) do
    {:producer, items}
  end

  def handle_demand(demand, items) when demand > 0 do
    {to_send, remaining} = Enum.split(items, demand)
    {:noreply, to_send, remaining}
  end
end

defmodule Pipeline.Consumer do
  use GenStage

  def start_link() do
    GenStage.start_link(__MODULE__, :ok)
  end

  def init(:ok) do
    {:consumer, :ok}
  end

  def handle_events(items, _from, state) do
    items
    |> Enum.map(&parse_item/1)
    |> Enum.filter(&valid?/1)
    |> Enum.each(&process/1)
    {:noreply, [], state}
  end
end

See Also

For more examples and patterns, see:

• meta-convert-dev - Foundational patterns with cross-language examples
• lang-clojure-dev - Clojure development patterns
• lang-elixir-dev - Elixir development patterns
• convert-erlang-elixir - Similar BEAM target conversion
• patterns-concurrency-dev - Async, processes, actors across languages
• patterns-serialization-dev - JSON, validation across languages
• patterns-metaprogramming-dev - Macros across languages