| name | convert-erlang-scala |
| description | Bidirectional conversion between Erlang and Scala. Use when migrating projects between these languages in either direction. Extends meta-convert-dev with Erlang↔Scala specific patterns. |
Erlang ↔ Scala Conversion
Overview
This skill guides the conversion of Erlang code to idiomatic Scala while maintaining functional programming principles, concurrent programming patterns via Akka, and fault-tolerance capabilities. Scala provides strong functional programming support combined with object-oriented features on the JVM, making it suitable for porting Erlang applications while gaining access to the extensive Java/Scala ecosystem.
Key Language Differences
Type Systems
- Erlang: Dynamic typing with pattern matching
- Scala: Static typing with type inference, algebraic data types (sealed traits), and comprehensive pattern matching
Concurrency Models
- Erlang: Actor model with lightweight processes (BEAM VM), message passing, process isolation
- Scala: Akka actors (JVM-based), futures/promises, parallel collections
Runtime Environment
- Erlang: BEAM VM with hot code swapping, distributed computing, per-process garbage collection
- Scala: JVM with comprehensive standard library, Akka for distributed systems, shared heap garbage collection
Core Conversion Patterns
1. Module and Function Definitions
Erlang:
-module(calculator).
-export([add/2, multiply/2, power/2]).
add(X, Y) -> X + Y.
multiply(X, Y) -> X * Y.
power(X, N) when N > 0 -> X * power(X, N - 1);
power(_, 0) -> 1.
Scala:
object Calculator {
def add(x: Int, y: Int): Int = x + y
def multiply(x: Int, y: Int): Int = x * y
def power(x: Int, n: Int): Int = n match {
case 0 => 1
case n if n > 0 => x * power(x, n - 1)
case _ => throw new IllegalArgumentException("Negative exponent")
}
}
2. Pattern Matching and Guards
Erlang:
-spec classify(number()) -> atom().
classify(N) when N < 0 -> negative;
classify(0) -> zero;
classify(N) when N > 0 -> positive.
process_result({ok, Value}) -> {success, Value};
process_result({error, Reason}) -> {failure, Reason};
process_result(_) -> unknown.
Scala:
def classify(n: Int): Symbol = n match {
case n if n < 0 => 'negative
case 0 => 'zero
case n if n > 0 => 'positive
}
// Using sealed traits for better type safety
sealed trait Result[+A]
case class Ok[A](value: A) extends Result[A]
case class Error(reason: String) extends Result[Nothing]
sealed trait ProcessedResult
case class Success(value: Any) extends ProcessedResult
case class Failure(reason: String) extends ProcessedResult
case object Unknown extends ProcessedResult
def processResult(result: Result[Any]): ProcessedResult = result match {
case Ok(value) => Success(value)
case Error(reason) => Failure(reason)
}
3. Records to Case Classes
Erlang:
-record(person, {name, age, email}).
create_person(Name, Age, Email) ->
#person{name=Name, age=Age, email=Email}.
get_name(#person{name=Name}) -> Name.
update_email(Person, NewEmail) ->
Person#person{email=NewEmail}.
Scala:
case class Person(name: String, age: Int, email: String)
def createPerson(name: String, age: Int, email: String): Person =
Person(name, age, email)
def getName(person: Person): String = person.name
def updateEmail(person: Person, newEmail: String): Person =
person.copy(email = newEmail)
4. Lists and List Operations
Erlang:
% List comprehensions
double_list(List) -> [X * 2 || X <- List].
filter_even(List) -> [X || X <- List, X rem 2 =:= 0].
% Recursive list processing
sum([]) -> 0;
sum([H|T]) -> H + sum(T).
map(_, []) -> [];
map(F, [H|T]) -> [F(H) | map(F, T)].
Scala:
// List operations with higher-order functions
def doubleList(list: List[Int]): List[Int] =
list.map(_ * 2)
// or: for (x <- list) yield x * 2
def filterEven(list: List[Int]): List[Int] =
list.filter(_ % 2 == 0)
// or: for (x <- list if x % 2 == 0) yield x
// Recursive list processing
def sum(list: List[Int]): Int = list match {
case Nil => 0
case h :: t => h + sum(t)
}
def map[A, B](f: A => B, list: List[A]): List[B] = list match {
case Nil => Nil
case h :: t => f(h) :: map(f, t)
}
// Built-in alternatives (preferred)
val summed = list.sum
val mapped = list.map(f)
5. Higher-Order Functions and Lambdas
Erlang:
apply_twice(F, X) -> F(F(X)).
% Anonymous functions
Increment = fun(X) -> X + 1 end,
Result = apply_twice(Increment, 5). % Result = 7
% Partial application
add(X, Y) -> X + Y.
add_five(X) -> add(5, X).
Scala:
def applyTwice[A](f: A => A, x: A): A = f(f(x))
// Anonymous functions
val increment: Int => Int = _ + 1
val result = applyTwice(increment, 5) // result = 7
// Partial application
def add(x: Int, y: Int): Int = x + y
def addFive: Int => Int = add(5, _)
// or: val addFive = (x: Int) => add(5, x)
6. Actor Model / Process Communication
Erlang:
-module(counter).
-export([start/0, increment/1, get_value/1, loop/1]).
start() ->
spawn(fun() -> loop(0) end).
increment(Pid) ->
Pid ! {increment, self()},
receive
{ok, NewValue} -> NewValue
after 5000 ->
timeout
end.
get_value(Pid) ->
Pid ! {get, self()},
receive
{value, V} -> V
after 5000 ->
timeout
end.
loop(Count) ->
receive
{increment, From} ->
NewCount = Count + 1,
From ! {ok, NewCount},
loop(NewCount);
{get, From} ->
From ! {value, Count},
loop(Count);
stop ->
ok
end.
Scala (Akka):
import akka.actor._
import akka.pattern.ask
import akka.util.Timeout
import scala.concurrent.duration._
import scala.concurrent.{Await, Future}
class Counter extends Actor {
private var count = 0
def receive: Receive = {
case Increment =>
count += 1
sender() ! Ok(count)
case Get =>
sender() ! Value(count)
case Stop =>
context.stop(self)
}
}
// Message definitions
case object Increment
case object Get
case class Ok(value: Int)
case class Value(count: Int)
case object Stop
// Usage
object CounterExample extends App {
val system = ActorSystem("CounterSystem")
val counter = system.actorOf(Props[Counter], "counter")
implicit val timeout: Timeout = 5.seconds
import system.dispatcher
// Fire-and-forget (like Erlang's !)
counter ! Increment
// Request-response (like Erlang's receive)
val future: Future[Value] = (counter ? Get).mapTo[Value]
val Value(count) = Await.result(future, 5.seconds)
counter ! Stop
system.terminate()
}
7. gen_server Pattern
Erlang:
-module(kv_store).
-behaviour(gen_server).
-export([start_link/0, get/1, put/2]).
-export([init/1, handle_call/3, handle_cast/2, terminate/2]).
start_link() ->
gen_server:start_link({local, ?MODULE}, ?MODULE, [], []).
get(Key) ->
gen_server:call(?MODULE, {get, Key}).
put(Key, Value) ->
gen_server:cast(?MODULE, {put, Key, Value}).
init([]) ->
{ok, #{}}.
handle_call({get, Key}, _From, State) ->
Result = maps:get(Key, State, undefined),
{reply, Result, State}.
handle_cast({put, Key, Value}, State) ->
NewState = maps:put(Key, Value, State),
{noreply, NewState}.
terminate(_Reason, _State) ->
ok.
Scala (Akka):
import akka.actor._
class KVStore extends Actor {
private var state: Map[String, Any] = Map.empty
def receive: Receive = {
case Get(key) =>
sender() ! state.get(key)
case Put(key, value) =>
state = state + (key -> value)
}
override def postStop(): Unit = {
// Cleanup logic (like terminate/2)
println("KVStore stopped")
}
}
case class Get(key: String)
case class Put(key: String, value: Any)
// Usage
object KVStoreExample extends App {
val system = ActorSystem("KVSystem")
val kvStore = system.actorOf(Props[KVStore], "kvStore")
// Cast-like (fire-and-forget)
kvStore ! Put("name", "Scala")
kvStore ! Put("version", 3)
// Call-like (request-response)
import akka.pattern.ask
import akka.util.Timeout
import scala.concurrent.duration._
import scala.concurrent.Await
implicit val timeout: Timeout = 5.seconds
val future = (kvStore ? Get("name")).mapTo[Option[Any]]
val result = Await.result(future, 5.seconds)
println(s"Result: $result")
system.terminate()
}
8. Supervision and Fault Tolerance
Erlang:
-module(my_supervisor).
-behaviour(supervisor).
-export([start_link/0, init/1]).
start_link() ->
supervisor:start_link({local, ?MODULE}, ?MODULE, []).
init([]) ->
ChildSpecs = [
#{id => worker1,
start => {my_worker, start_link, []},
restart => permanent,
shutdown => 5000,
type => worker},
#{id => worker2,
start => {my_worker, start_link, []},
restart => transient,
shutdown => 5000,
type => worker}
],
{ok, {#{strategy => one_for_one,
intensity => 5,
period => 10}, ChildSpecs}}.
Scala (Akka):
import akka.actor._
import scala.concurrent.duration._
class MySupervisor extends Actor {
override val supervisorStrategy = OneForOneStrategy(
maxNrOfRetries = 5,
withinTimeRange = 10.seconds
) {
case _: ArithmeticException => SupervisorStrategy.Resume
case _: NullPointerException => SupervisorStrategy.Restart
case _: IllegalArgumentException => SupervisorStrategy.Stop
case _: Exception => SupervisorStrategy.Escalate
}
val worker1 = context.actorOf(Props[MyWorker], "worker1")
val worker2 = context.actorOf(Props[MyWorker], "worker2")
def receive: Receive = {
case msg => worker1 ! msg
}
}
class MyWorker extends Actor {
def receive: Receive = {
case work: Work => processWork(work)
}
def processWork(work: Work): Unit = {
// Work processing logic
println(s"Processing: $work")
}
override def preStart(): Unit = {
println(s"${self.path.name} starting")
}
override def postRestart(reason: Throwable): Unit = {
println(s"${self.path.name} restarted due to: $reason")
}
}
case class Work(data: String)
9. Error Handling
Erlang:
safe_divide(_, 0) -> {error, division_by_zero};
safe_divide(X, Y) -> {ok, X / Y}.
% Try-catch
try_operation(Data) ->
try
risky_function(Data)
catch
error:badarg -> {error, bad_argument};
error:Reason -> {error, Reason};
throw:Value -> {thrown, Value}
end.
% Let it crash philosophy
process_data(Data) ->
% Process crashes if something goes wrong
transform(Data).
Scala:
// Using Either for explicit error handling
sealed trait DivisionError
case object DivisionByZero extends DivisionError
def safeDivide(x: Double, y: Double): Either[DivisionError, Double] =
if (y == 0) Left(DivisionByZero)
else Right(x / y)
// Using Option for simple cases
def safeDivideOption(x: Double, y: Double): Option[Double] =
if (y == 0) None
else Some(x / y)
// Try-catch for exception handling
import scala.util.{Try, Success, Failure}
def tryOperation(data: String): Either[String, Int] =
Try(riskyFunction(data)) match {
case Success(value) => Right(value)
case Failure(ex: IllegalArgumentException) => Left("bad_argument")
case Failure(ex) => Left(ex.getMessage)
}
// For-comprehension for chaining operations
def complexOperation(x: Int, y: Int): Either[String, Int] = for {
divided <- safeDivide(x.toDouble, y.toDouble).left.map(_.toString)
result <- Right(divided.toInt)
} yield result
10. Binary Pattern Matching
Erlang:
parse_header(<<Type:8, Length:16/big, Rest/binary>>) ->
{Type, Length, Rest}.
parse_packet(<<Magic:32/big, Version:8, Data/binary>>) ->
{Magic, Version, Data}.
encode_header(Type, Length, Data) ->
<<Type:8, Length:16/big, Data/binary>>.
Scala:
import akka.util.ByteString
import java.nio.ByteOrder
// Using Akka ByteString
def parseHeader(bytes: ByteString): Option[(Byte, Short, ByteString)] = {
if (bytes.length < 3) None
else {
val iter = bytes.iterator
val typ = iter.getByte
val length = iter.getShort(ByteOrder.BIG_ENDIAN)
val rest = bytes.drop(3)
Some((typ, length, rest))
}
}
// Using scodec for complex binary protocols
import scodec._
import scodec.bits._
import scodec.codecs._
case class Header(typ: Int, length: Int, data: ByteVector)
val headerCodec: Codec[Header] =
(uint8 :: uint16 :: bytes).as[Header]
// Encoding
val encoded: BitVector = headerCodec.encode(Header(1, 256, hex"deadbeef")).require
// Decoding
val decoded: Header = headerCodec.decode(encoded).require.value
11. ETS Tables to Concurrent Collections
Erlang:
start() ->
ets:new(cache, [named_table, public, set]),
ok.
insert(Key, Value) ->
ets:insert(cache, {Key, Value}),
ok.
lookup(Key) ->
case ets:lookup(cache, Key) of
[{Key, Value}] -> {ok, Value};
[] -> {error, not_found}
end.
delete(Key) ->
ets:delete(cache, Key),
ok.
Scala:
import java.util.concurrent.ConcurrentHashMap
import scala.jdk.CollectionConverters._
object Cache {
private val cache = new ConcurrentHashMap[String, Any]().asScala
def insert(key: String, value: Any): Unit =
cache.put(key, value)
def lookup(key: String): Option[Any] =
cache.get(key)
def delete(key: String): Unit =
cache.remove(key)
}
// Or using an actor for state management
class CacheActor extends Actor {
private var cache: Map[String, Any] = Map.empty
def receive: Receive = {
case Insert(k, v) =>
cache = cache + (k -> v)
sender() ! Done
case Lookup(k) =>
sender() ! cache.get(k)
case Delete(k) =>
cache = cache - k
sender() ! Done
}
}
case class Insert(key: String, value: Any)
case class Lookup(key: String)
case class Delete(key: String)
case object Done
12. Distributed Erlang to Akka Cluster
Erlang:
% Send message to named process on remote node
send_to_node(Node, ProcessName, Message) ->
{ProcessName, Node} ! Message.
% Register process globally
register_globally(Name, Pid) ->
global:register_name(Name, Pid).
% Call remote process
call_remote(Node, Module, Function, Args) ->
rpc:call(Node, Module, Function, Args).
Scala (Akka Cluster):
import akka.actor._
import akka.cluster.Cluster
import akka.cluster.routing._
// Remote actor communication
object DistributedExample {
def sendToRemote(system: ActorSystem, path: String, message: Any): Unit = {
val selection = system.actorSelection(path)
selection ! message
}
// Cluster-aware routing
def createClusterRouter(system: ActorSystem): ActorRef = {
system.actorOf(
ClusterRouterPool(
local = akka.routing.RoundRobinPool(5),
settings = ClusterRouterPoolSettings(
totalInstances = 20,
maxInstancesPerNode = 5,
allowLocalRoutees = true
)
).props(Props[Worker]),
name = "workerRouter"
)
}
}
// Cluster singleton for global registration
import akka.cluster.singleton._
object SingletonExample {
def createSingleton(system: ActorSystem): ActorRef = {
system.actorOf(
ClusterSingletonManager.props(
singletonProps = Props[GlobalRegistry],
terminationMessage = PoisonPill,
settings = ClusterSingletonManagerSettings(system)
),
name = "globalRegistry"
)
}
}
class GlobalRegistry extends Actor {
private var registry: Map[String, ActorRef] = Map.empty
def receive: Receive = {
case Register(name, ref) =>
registry = registry + (name -> ref)
sender() ! Registered
case Lookup(name) =>
sender() ! registry.get(name)
}
}
case class Register(name: String, ref: ActorRef)
case class Lookup(name: String)
case object Registered
Conversion Strategy
Step 1: Analyze Erlang Codebase
- Identify module structure and dependencies
- Map OTP behaviors (gen_server, gen_statem, gen_event, supervisor)
- Document message-passing patterns and process hierarchies
- List external dependencies and find Scala/Java equivalents
- Analyze distributed Erlang usage
Step 2: Design Scala Architecture
- Plan package organization and module structure
- Design type hierarchy using sealed traits and case classes
- Choose concurrency framework (Akka actors, Akka Typed, or Cats Effect)
- Select fault-tolerance strategy (Akka supervision or custom)
- Plan distributed system architecture (Akka Cluster, gRPC)
Step 3: Convert Core Logic
- Start with pure functions and data structures
- Convert pattern matching to Scala's match expressions
- Translate list operations to Scala collections
- Migrate error handling to Either/Option types or custom ADTs
- Convert records to case classes
Step 4: Implement Concurrency
- Replace spawn/receive with Akka actors
- Convert gen_server to actor-based patterns
- Implement supervision hierarchies with Akka supervision strategies
- Add lifecycle callbacks (preStart, postStop, postRestart)
Step 5: Handle Distribution
- Implement Akka Cluster for distributed scenarios
- Set up cluster routing and sharding
- Configure cluster singleton for global state
- Implement serialization for remote messages
Step 6: Testing and Validation
- Port EUnit/CommonTest to ScalaTest or Specs2
- Test concurrent behaviors with Akka TestKit
- Validate message-passing semantics
- Property-based testing with ScalaCheck
- Performance testing and JVM tuning
Common Libraries and Equivalents
| Erlang | Scala / JVM Equivalent |
|---|
| gen_server | Akka actors, Akka Typed |
| supervisor | Akka supervision |
| gen_statem | Akka FSM, Akka Typed behaviors |
| ETS | ConcurrentHashMap, Caffeine cache |
| Mnesia | Slick, Doobie, Quill (SQL databases) |
| httpc, hackney | Akka HTTP client, http4s, sttp |
| cowboy | Akka HTTP, http4s, Play Framework |
| jsx, jiffy (JSON) | Circe, Play JSON, spray-json |
| lager (logging) | Logback, Log4j2, scala-logging |
| poolboy | Akka routing, HikariCP (DB) |
| riak_core | Akka Cluster Sharding |
Best Practices
1. Embrace Static Typing
- Use Scala's type system to catch errors at compile time
- Define sealed traits for algebraic data types
- Use type parameters and variance for generic code
- Leverage type classes (implicits) for polymorphism
2. Preserve Functional Patterns
- Keep functions pure where possible
- Use immutable data structures by default
- Leverage for-comprehensions for sequential operations
- Use pattern matching extensively
3. Adapt Concurrency Models
- Use Akka actors for actor-like behavior
- Consider Akka Typed for better type safety
- Use futures for asynchronous computations
- Implement backpressure with Akka Streams
4. Supervision Strategies
- Design supervision hierarchies carefully
- Use different strategies per error type
- Implement lifecycle hooks (preStart, postRestart)
- Monitor critical actors with death watch
5. Error Handling
- Prefer Either and Option over exceptions
- Use Try for exception-throwing operations
- Design error ADTs with sealed traits
- Use for-comprehensions for error propagation
6. Performance Considerations
- Profile JVM performance regularly
- Tune garbage collection settings
- Use specialized collections where appropriate
- Consider Akka Streams for backpressure
- Benchmark actor mailbox sizes
7. Testing
- Write unit tests with ScalaTest or Specs2
- Use Akka TestKit for actor testing
- Property-based testing with ScalaCheck
- Integration testing for distributed scenarios
Example: Complete Application Conversion
Erlang Chat Server
-module(chat_server).
-behaviour(gen_server).
-export([start_link/0, join/2, leave/1, send_message/2]).
-export([init/1, handle_call/3, handle_cast/2, terminate/2]).
-record(state, {users = #{}}).
start_link() ->
gen_server:start_link({local, ?MODULE}, ?MODULE, [], []).
init([]) ->
{ok, #state{}}.
handle_call({join, Username, Pid}, _From, State = #state{users = Users}) ->
monitor(process, Pid),
NewUsers = Users#{Username => Pid},
notify_all(NewUsers, {user_joined, Username}),
{reply, ok, State#state{users = NewUsers}};
handle_call({leave, Username}, _From, State = #state{users = Users}) ->
NewUsers = maps:remove(Username, Users),
notify_all(NewUsers, {user_left, Username}),
{reply, ok, State#state{users = NewUsers}}.
handle_cast({send_message, From, Message}, State = #state{users = Users}) ->
notify_all(Users, {message, From, Message}),
{noreply, State}.
terminate(_Reason, _State) ->
ok.
notify_all(Users, Msg) ->
maps:foreach(fun(_, Pid) -> Pid ! Msg end, Users).
join(Username, Pid) ->
gen_server:call(?MODULE, {join, Username, Pid}).
leave(Username) ->
gen_server:call(?MODULE, {leave, Username}).
send_message(From, Message) ->
gen_server:cast(?MODULE, {send_message, From, Message}).
Scala Chat Server
import akka.actor._
import scala.collection.immutable.Map
class ChatServer extends Actor {
private var users: Map[String, ActorRef] = Map.empty
def receive: Receive = {
case Join(username, userRef) =>
context.watch(userRef)
users = users + (username -> userRef)
notifyAll(UserJoined(username))
sender() ! Joined
case Leave(username) =>
users.get(username).foreach(context.unwatch)
users = users - username
notifyAll(UserLeft(username))
sender() ! Left
case SendMessage(from, message) =>
notifyAll(Message(from, message))
case Terminated(userRef) =>
users.find(_._2 == userRef).foreach { case (username, _) =>
users = users - username
notifyAll(UserLeft(username))
}
}
private def notifyAll(msg: ChatEvent): Unit = {
users.values.foreach(_ ! msg)
}
override def postStop(): Unit = {
println("Chat server stopped")
}
}
// Message protocol
sealed trait ChatCommand
case class Join(username: String, userRef: ActorRef) extends ChatCommand
case class Leave(username: String) extends ChatCommand
case class SendMessage(from: String, message: String) extends ChatCommand
sealed trait ChatResponse
case object Joined extends ChatResponse
case object Left extends ChatResponse
sealed trait ChatEvent
case class UserJoined(username: String) extends ChatEvent
case class UserLeft(username: String) extends ChatEvent
case class Message(from: String, text: String) extends ChatEvent
// User client actor
class ChatClient(username: String, server: ActorRef) extends Actor {
override def preStart(): Unit = {
server ! Join(username, self)
}
def receive: Receive = {
case Joined =>
println(s"$username joined the chat")
case UserJoined(user) =>
println(s"$user joined")
case UserLeft(user) =>
println(s"$user left")
case Message(from, text) =>
println(s"[$from]: $text")
case SendMsg(text) =>
server ! SendMessage(username, text)
}
override def postStop(): Unit = {
server ! Leave(username)
}
}
case class SendMsg(text: String)
// Usage example
object ChatExample extends App {
val system = ActorSystem("ChatSystem")
val server = system.actorOf(Props[ChatServer], "server")
val alice = system.actorOf(Props(new ChatClient("Alice", server)), "alice")
val bob = system.actorOf(Props(new ChatClient("Bob", server)), "bob")
Thread.sleep(100)
alice ! SendMsg("Hello everyone!")
bob ! SendMsg("Hi Alice!")
Thread.sleep(1000)
system.terminate()
}
Advanced Topics
Hot Code Swapping
Erlang's hot code swapping has limited JVM equivalents:
- JRebel: Commercial tool for class reloading
- sbt-revolver: Development-time hot reloading
- Akka Rolling Updates: For production deployments
- Containerized deployments: Blue-green or canary deployments
- Feature flags: Toggle functionality without redeployment
Process Migration
For Erlang's process migration:
- Akka Cluster Sharding: Automatic entity rebalancing
- Akka Cluster Singleton: Migrate singleton across nodes
- Akka Persistence: State recovery after migration
- Custom serialization: Efficient message serialization
Binary Protocols
For complex binary protocols:
- scodec: Composable binary codecs
- Akka ByteString: Efficient binary operations
- java.nio.ByteBuffer: Low-level binary handling
- Protocol Buffers: Schema-based serialization
Distributed Tracing
Monitor distributed systems:
- Kamon: Metrics and tracing for Akka
- OpenTelemetry: Distributed tracing standard
- Zipkin: Distributed tracing system
- Jaeger: Distributed tracing platform
Troubleshooting
Common Issues
Issue: Actor mailbox overflow
- Solution: Implement bounded mailboxes, backpressure, or use Akka Streams
Issue: Memory leaks in long-running actors
- Solution: Implement state cleanup, use Akka Timers for periodic cleanup
Issue: Shared mutable state
- Solution: Encapsulate all mutable state within actors, use immutable messages
Issue: JVM garbage collection pauses
- Solution: Tune GC settings, use G1GC or ZGC, reduce allocation rate
Issue: Supervision strategy not triggering
- Solution: Ensure exceptions are thrown (not caught), verify supervisor hierarchy
Issue: Cluster split-brain
- Solution: Configure Akka Split Brain Resolver, use lease-based strategies
Issue: Serialization errors in distributed setup
- Solution: Configure serialization bindings, use Protocol Buffers or Avro
Issue: Performance degradation under load
- Solution: Profile with JMC/VisualVM, tune dispatcher settings, use routing
References
Official Documentation
Libraries
Learning Resources
- "Programming in Scala" by Martin Odersky
- "Akka in Action" by Raymond Roestenburg
- "Functional Programming in Scala" by Paul Chiusano
- "Reactive Messaging Patterns with the Actor Model" by Vaughn Vernon
- Scala documentation: https://docs.scala-lang.org/
- Akka documentation: https://doc.akka.io/
Tools
- sbt: Scala build tool
- ScalaTest/Specs2: Testing frameworks
- ScalaCheck: Property-based testing
- Metals: Scala language server
- IntelliJ IDEA: IDE with Scala support
