How can you achieve multithreading in Scala?

On the JVM, Scala can use Java threads, executors and synchronization primitives. Scala futures provide result-oriented composition on an ExecutionContext; libraries such as Akka add message-oriented concurrency. The choice depends on ownership, blocking behavior, load and failure handling.

A small direct-thread example uses an atomic counter and joins both workers before reading the result:

import java.util.concurrent.atomic.AtomicInteger
@main def threadsExample(): Unit = {
  val count = new AtomicInteger(0)
  def work(): Unit = {
    var i = 0
    while (i < 1000) { count.incrementAndGet(); i += 1 }
  }
  val first = new Thread(() => work())
  val second = new Thread(() => work())
  first.start()
  second.start()
  first.join()
  second.join()
  assert(count.get() == 2000)
}

An ordinary shared var increment would not be atomic. This example creates only two workers; creating an unbounded thread per request is not a general server design. Interruption and failures also need deliberate handling in a real application. Atomic integers

For multiple tasks, use an executor whose capacity, queueing, rejection and shutdown policies suit the workload. Compose futures without blocking request or event-loop threads, and isolate unavoidable blocking work. An execution context may use a pool or execute on a calling thread; asynchronous composition alone does not guarantee parallelism. CPU availability and contention affect whether concurrency helps. Execution contexts

Prefer ownership or immutable data where possible. Actors serialize processing within one actor, but mutable data shared outside that boundary still needs protection. Measure representative workload behavior rather than assuming extra threads increase throughput.