[SPARK-2490] Change recursive visiting on RDD dependencies to iterative approach


When performing some transformations on RDDs after many iterations, the dependencies of RDDs could be very long. It can easily cause StackOverflowError when recursively visiting these dependencies in Spark core. For example:

    var rdd = sc.makeRDD(Array(1))
    for (i <- 1 to 1000) { 
      rdd = rdd.coalesce(1).cache()
      rdd.collect()
    }

This PR changes recursive visiting on rdd's dependencies to iterative approach to avoid StackOverflowError. 

In addition to the recursive visiting, since the Java serializer has a known [bug](http://bugs.java.com/bugdatabase/view_bug.do?bug_id=4152790) that causes StackOverflowError too when serializing/deserializing a large graph of objects. So applying this PR only solves part of the problem. Using KryoSerializer to replace Java serializer might be helpful. However, since KryoSerializer is not supported for `spark.closure.serializer` now, I can not test if KryoSerializer can solve Java serializer's problem completely. 





Yes. There is a recursive calling chain formed by `getShuffleMapStage` calling `newOrUsedStage` that calls `newStage` that calls `getParentStages` that calls `getShuffleMapStage` again. The calling chain is going to fill `shuffleToMapStage`  for parent map stages. Here I use an iterative approach instead.