Spark 2.3: How to release RDD from memory in iterative algorithm

Taking an example code from https://livebook.manning.com/book/spark-graphx-in-action/chapter-6/1

import org.apache.spark.graphx._

def dijkstra[VD](g:Graph[VD,Double], origin:VertexId) = {
    var g2 = g.mapVertices(
        (vid,vd) => (false, if (vid == origin) 0.0 else Double.MaxValue,
            List[VertexId]()))
    for (i <- 1L to g.vertices.count-1) {
        val currentVertexId =
            g2.vertices.filter(!_._2._1) 
                .fold((0L,(false,Double.MaxValue,List[VertexId]())))((a,b) =>
                    if (a._2._2 < b._2._2) a else b)
                ._1
    val newDistances = g2.aggregateMessages[(Double,List[VertexId])]( 
        ctx => if (ctx.srcId == currentVertexId) 
            ctx.sendToDst((
                ctx.srcAttr._2 + ctx.attr,
                ctx.srcAttr._3 :+ ctx.srcId)),
                (a,b) => if (a._1 < b._1) a else b)
    g2 = g2.outerJoinVertices(newDistances)((vid, vd, newSum) => {
        val newSumVal =
            newSum.getOrElse((Double.MaxValue,List[VertexId]())) 
        (vd._1 || vid == currentVertexId,
            math.min(vd._2, newSumVal._1),
            if (vd._2 < newSumVal._1) vd._3 else newSumVal._2)})
    }
    g.outerJoinVertices(g2.vertices)((vid, vd, dist) =>
        (vd, dist.getOrElse((false,Double.MaxValue,List[VertexId]())).productIterator.toList.tail))
}

val myVertices = spark.sparkContext.makeRDD(Array((1L, "A"), (2L, "B"), (3L, "C"),
  (4L, "D"), (5L, "E"), (6L, "F"), (7L, "G")))
val myEdges = spark.sparkContext.makeRDD(Array(Edge(1L, 2L, 7.0), Edge(1L, 4L, 5.0),
  Edge(2L, 3L, 8.0), Edge(2L, 4L, 9.0), Edge(2L, 5L, 7.0),
  Edge(3L, 5L, 5.0), Edge(4L, 5L, 15.0), Edge(4L, 6L, 6.0),
  Edge(5L, 6L, 8.0), Edge(5L, 7L, 9.0), Edge(6L, 7L, 11.0)))
val myGraph = Graph(myVertices, myEdges)

val result = dijkstra(myGraph, 1L)

result.vertices.map(_._2).collect

Every time I run this code, the VertexRDD stays in memory and I cannot release it.

It seems like GraphX is caching the graph data even though it is not specified in the code. Is it possible to release the previous run's RDD data from memory?

I have tried to unpersist by doing result.unpersist(), result.vertices.unpersist(), result.edges.unpersist(), and even result.checkpoint().

Ultimately, I want to run the code in a for loop to find multiple results for different origin, and unless I can figure out how to release the RDDs from before, I come across memory issues.

Update: A brute force method I came up with to clear all VertexRDD and EdgeRDD

for ((k,v) <- spark.sparkContext.getPersistentRDDs) {
  val convertedToString = v.toString()
  if (convertedToString.contains("VertexRDD") || convertedToString.contains("EdgeRDD")) {
      v.unpersist()
  }
}