Convert Clojure to Elixir

Convert Clojure code to idiomatic Elixir. This skill extends meta-convert-dev with Clojure-to-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
• Reverse conversion (Elixir → Clojure) - see convert-elixir-clojure
• ClojureScript specifics - see convert-clojurescript-* skills

---

Quick Reference

Clojure	Elixir	Notes
nil	nil	Same concept, different runtime
true / false	true / false	Booleans as atoms
:keyword	:atom	Keywords → atoms
"string"	"string"	Both UTF-8, Elixir on binaries
[1 2 3]	[1, 2, 3]	Vectors → lists (different impl)
{:a 1 :b 2}	%{a: 1, b: 2}	Maps syntax differs
(defn f [x] x)	def f(x), do: x	Function definition
(fn [x] x)	fn x -> x end	Anonymous functions
->	|>	Threading/piping
atom	GenServer/Agent	State management
agent	Agent/GenServer	Async state
(ns my.ns)	defmodule My.Ns	Namespaces → modules

---

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

Clojure	Elixir	Notes
nil	nil	Null/absence indicator
true / false	true / false	Booleans (atoms)
Long / 42	integer	Arbitrary precision in both
Double / 3.14	float	64-bit double precision
BigDecimal	Decimal (library)	Use decimal package
:keyword	:atom	Keywords map to atoms
Symbol	Atom or String	Context-dependent
"string"	"string"	UTF-8 strings (binary in Elixir)
Character	Integer codepoint	Elixir: ?a = 97
Ratio (1/3)	Float or Ratio lib	Elixir: 1/3 is float division

Collection Types

Clojure	Elixir	Notes
[1 2 3] (vector)	[1, 2, 3] (list)	Clojure: O(1) index; Elixir: O(n) list
'(1 2 3) (list)	[1, 2, 3]	Both linked lists
{:a 1 :b 2} (map)	%{a: 1, b: 2}	Similar hash maps
#{1 2 3} (set)	MapSet.new([1, 2, 3])	Set implementations
(list 1 2 3)	[1, 2, 3]	Linked lists
(vector 1 2 3)	Tuple or List	Context: indexed → tuple, seq → list
Persistent queue	:queue module	Use Erlang's queue
LazySeq	Stream	Lazy evaluation

Composite Types

Clojure	Elixir	Notes
(defrecord User [name age])	defstruct [:name, :age]	Records → Structs
(deftype X [...])	defstruct or custom protocol	Type definitions
(defprotocol P ...)	defprotocol	Protocol-based polymorphism
(reify P ...)	Struct with protocol impl	Anonymous implementation
Metadata ^{:key val}	Module attributes @key val	Compile-time metadata
Tagged literal #inst	Custom sigil ~D[...]	Reader macros → sigils

---

Paradigm Translation

Mental Model Shift: Clojure/JVM → Elixir/BEAM

Clojure Concept	Elixir Approach	Key Insight
Persistent data structures	Immutable data by default	Both immutable, Elixir uses structural sharing
Atoms (thread-safe ref)	GenServer or Agent	Shared state → process with message passing
Refs (coordinated)	GenServer with transactions	STM → explicit serialization via GenServer
Agents (async)	Agent (similar!)	Very similar async state model
Futures	Task.async/await	Async computation patterns
core.async channels	GenStage or Broadway	Backpressure-aware streaming
Vars (dynamic binding)	Process dictionary (avoid)	Use explicit passing or Application env
Multimethods	Protocols	Polymorphism via protocols
Macros	Macros	Both have powerful macro systems

Concurrency Mental Model

Clojure Model	Elixir Model	Conceptual Translation
Atom (swap!)	Agent.update/GenServer.call	Synchronous state mutation → process message
Ref (dosync)	GenServer with transaction fn	Coordinated refs → single process serializes
Agent (send)	Agent.cast	Async state update → nearly 1:1
Future (future ...)	Task.async	One-off async computation
core.async go blocks	spawn/GenServer	CSP channels → actor model
Thread pool	Supervisor + workers	OTP supervision for fault tolerance

---

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

Pattern	Clojure	Elixir
Async task	(future ...)	Task.async
Thread pool	pmap	Task.async_stream
Channels	core.async channels	GenStage/Broadway
Shared state	Atom	Agent or GenServer
Transaction	Refs + dosync	GenServer with state fn
Fire and forget	Agent send	Agent.cast or GenServer.cast

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

Task	Clojure	Elixir
Create project	lein new app myapp	mix new myapp
Dependencies	lein deps	mix deps.get
REPL	lein repl	iex -S mix
Run	lein run	mix run
Test	lein test	mix test
Build JAR	lein uberjar	mix release

---

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

Tool	Purpose	Notes
Mix	Build tool, deps, tasks	Replaces Leiningen
IEx	Interactive shell (REPL)	Replaces Clojure REPL
ExUnit	Testing framework	Replaces clojure.test
Dialyzer	Static type checker	Optional, uses typespecs
Credo	Code linting	Quality checks
ExDoc	Documentation generator	Generates HTML docs from @doc

---

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