Question

Scala的执行上下文为

import scala.concurrent.ExecutionContext.Implicits.global

Ans Play有自己的执行上下文

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

主要区别是什么？我们应该使用哪一个，哪个是哪个。

Answer 1

scala.concurrent.ExecutionContext.Implicits.global（Scala std lib执行上下文）是标准scala库提供的执行上下文。它是一个特殊的ForkJoinPool，它使用阻塞方法来处理可能阻塞的代码，以便在池中生成新线程。你不应该在播放应用程序中使用它，因为播放将无法控制它。 play.api.libs.concurrent.Execution.Implicits.defaultContext（播放执行上下文）使用actor dispatcher的位置。这是播放应用程序应该使用的内容。除了播放执行上下文之外，最好将阻塞调用卸载到不同的执行上下文。这样就可以避免播放应用程序进入饥饿状态。

播放执行上下文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())

当app不为null时，它使用actorSystem.dispatcher

Scala标准执行上下文。

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

此方法创建执行服务，并考虑available processors和读取配置。

  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(
        desiredParallelism,
        threadFactory,
        uncaughtExceptionHandler,
        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")
        t.printStackTrace(System.err)
        val exec = new ThreadPoolExecutor(
          desiredParallelism,
          desiredParallelism,
          5L,
          TimeUnit.MINUTES,
          new LinkedBlockingQueue[Runnable],
          threadFactory
        )
        exec.allowCoreThreadTimeOut(true)
        exec
    }
  }

此代码负责托管阻止。在代码中遇到blocking时尝试创建新线程。

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

    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 }
            true
          }
          override def isReleasable = isdone
        })
        result
      }
    })
  }

scala的执行上下文和play的执行上下文之间有什么区别

1 个答案: