Из-Под Глыб

"Spark is faster than map/reduce". A guy working at Databricks is giving a talk regarding how Spark works.

Omfg; how can one possibly deal with all this?

UPD. From the same talk. cartesion()

  /**
   * Returns a Seq of the children of this node.
   * Children should not change. Immutability required for containsChild optimization
   */
  def children: Seq[BaseType]

  lazy val containsChild: Set[TreeNode[_]] = children.toSet

Imagine, we override the class, but our children are not to be mutable. Why t.f. then not declare it val?!

Another masterpiece

object CurrentOrigin {
  private val value = new ThreadLocal[Origin]() {
    override def initialValue: Origin = Origin()
  }

  def get: Origin = value.get()
  def set(o: Origin): Unit = value.set(o)

  def reset(): Unit = value.set(Origin())

  def setPosition(line: Int, start: Int): Unit = {
    value.set(
      value.get.copy(line = Some(line), startPosition = Some(start)))
  }

  def withOrigin[A](o: Origin)(f: => A): A = {
    set(o)
    val ret = try f finally { reset() }
    reset()
    ret
  }
}

So, we have a static object that wants to do something with a context. (See withOrigin.) That's "dependency injection", Fortran style. And we can do without vars, because, well, it's ThreadLocal.

Who wrote all this... could they hire Scala programmers, I wonder...