RDD里的模式匹配：

def hasNext: Boolean = (thisIter.hasNext, otherIter.hasNext) match {

          case (true, true) => true

          case (false, false) => false

          case _ => throw new SparkException("Can only zip RDDs with " +

            "same number of elements in each partition")

        }

jobResult = jobResult match {

          case Some(value) => Some(f(value, taskResult.get))

          case None => taskResult

        }

    take(1) match {

      case Array(t) => t

      case _ => throw new UnsupportedOperationException("empty collection")

    }

下面的比较好理解：

val len = rdd.dependencies.length

      len match {

        case 0 => Seq.empty

        case 1 =>

          val d = rdd.dependencies.head

          debugString(d.rdd, prefix, d.isInstanceOf[ShuffleDependency[_, _, _]], true)

        case _ => //所有的都到碗里来

          val frontDeps = rdd.dependencies.take(len - 1)

          val frontDepStrings = frontDeps.flatMap(

            d => debugString(d.rdd, prefix, d.isInstanceOf[ShuffleDependency[_, _, _]]))

          val lastDep = rdd.dependencies.last

          val lastDepStrings =

            debugString(lastDep.rdd, prefix, lastDep.isInstanceOf[ShuffleDependency[_, _, _]], true)

          (frontDepStrings ++ lastDepStrings)

      }

Spark1.4源码走读笔记之模式匹配