Arpit Suthar Arpit Suthar - 1 year ago 106
Scala Question

What is the difference between scala's Execution Context and play's Execution Context

Scala has its Execution Context as


Ans Play has its own Execution Context

import play.api.libs.concurrent.Execution.Implicits.defaultContext

Whats is the main difference and which one should we use and in which senario.

Answer Source (Scala std lib execution context) is the execution context provided by standard scala library. It is a special ForkJoinPool that using the blocking method to handle potentially blocking code in order to spawn new threads in the pool. You should not use this inside a play application as play will not have any control over it. Where as play.api.libs.concurrent.Execution.Implicits.defaultContext (Play execution context) uses actor dispatcher. This is what should be used for play applications. Also it is good practice to offload blocking calls to different execution context other than play execution context. This way it will avoid play app running into starvation state.

Play execution context impl play.api.libs.concurrent.Execution.Implicits.defaultContext

 val appOrNull: Application = Play._currentApp
 appOrNull match {
  case null => common
  case app: Application => app.actorSystem.dispatcher

 private val common = ExecutionContext.fromExecutor(new ForkJoinPool())

When app is not null it uses actorSystem.dispatcher

Scala standard execution context.

val executor: Executor = es match {
    case null => createExecutorService
    case some => some

This method creates the executor service taking into account available processors and reading configuration.

  def createExecutorService: ExecutorService = {

    def getInt(name: String, default: String) = (try System.getProperty(name, default) catch {
      case e: SecurityException => default
    }) match {
      case s if s.charAt(0) == 'x' => (Runtime.getRuntime.availableProcessors * s.substring(1).toDouble).ceil.toInt
      case other => other.toInt

    def range(floor: Int, desired: Int, ceiling: Int) = scala.math.min(scala.math.max(floor, desired), ceiling)

    val desiredParallelism = range(
      getInt("scala.concurrent.context.minThreads", "1"),
      getInt("scala.concurrent.context.numThreads", "x1"),
      getInt("scala.concurrent.context.maxThreads", "x1"))

    val threadFactory = new DefaultThreadFactory(daemonic = true)

    try {
      new ForkJoinPool(
        true) // Async all the way baby
    } catch {
      case NonFatal(t) =>
        System.err.println("Failed to create ForkJoinPool for the default ExecutionContext, falling back to ThreadPoolExecutor")
        val exec = new ThreadPoolExecutor(
          new LinkedBlockingQueue[Runnable],

This code is responsible for managed blocking. tries to create a new thread when blocking is encountered in the code.

// Implement BlockContext on FJP threads
  class DefaultThreadFactory(daemonic: Boolean) extends ThreadFactory with ForkJoinPool.ForkJoinWorkerThreadFactory {
    def wire[T <: Thread](thread: T): T = {

    def newThread(runnable: Runnable): Thread = wire(new Thread(runnable))

    def newThread(fjp: ForkJoinPool): ForkJoinWorkerThread = wire(new ForkJoinWorkerThread(fjp) with BlockContext {
      override def blockOn[T](thunk: =>T)(implicit permission: CanAwait): T = {
        var result: T = null.asInstanceOf[T]
        ForkJoinPool.managedBlock(new ForkJoinPool.ManagedBlocker {
          @volatile var isdone = false
          override def block(): Boolean = {
            result = try thunk finally { isdone = true }
          override def isReleasable = isdone
Recommended from our users: Dynamic Network Monitoring from WhatsUp Gold from IPSwitch. Free Download