LearningSlowly - 1 year ago 84
Scala Question

# GraphX - Weighted shortest path implementation - java.lang.NoSuchMethodError

Edit - I discovered that the book was written for scala

`1.6`
but the remainder is
`2.11`
.

I am trying to implement a weighted shortest path algorithm from Michael Malak and Robin East's
`Spark GraphX in Action`
book. The part in question is Listing 6.4 "Executing the shortest path algorithm that uses breadcrumbs" from Chapter 6 here.

I have my own graph that I create from two RDDs. There are
`344436`
vertices and
`772983`
edges. I can perform an unweighted shortest path computation using the native GraphX library and I'm confident in the graph construction.

In this case I use their Dijkstra's implementation as follows:

``````val my_graph: Graph[(Long),Double] = Graph.apply(verticesRDD, edgesRDD).cache()

def dijkstra[VD](g:Graph[VD,Double], origin:VertexId) = {
var g2 = g.mapVertices(
(vid,vd) => (false, if (vid == origin) 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
))
}

//  Path Finding - random node from which to find all paths
val v1 = 4000000028222916L
``````

I then call their function with my graph and a random vertex ID. Previously I had issues with
`v1`
not being recognised as
`long`
type and the
`L`
suffix solved this.

``````val results = dijkstra(my_graph, 1L).vertices.map(_._2).collect

println(results)
``````

However, this returns the following:

``````Error: Exception in thread "main" java.lang.NoSuchMethodError: scala.runtime.ObjectRef.create(Ljava/lang/Object;)Lscala/runtime/ObjectRef;
at GraphX\$.dijkstra\$1(GraphX.scala:51)
at GraphX\$.main(GraphX.scala:85)
at GraphX.main(GraphX.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.spark.deploy.SparkSubmit\$.org\$apache\$spark\$deploy\$SparkSubmit\$\$runMain(SparkSubmit.scala:731)
at org.apache.spark.deploy.SparkSubmit\$.doRunMain\$1(SparkSubmit.scala:181)
at org.apache.spark.deploy.SparkSubmit\$.submit(SparkSubmit.scala:206)
at org.apache.spark.deploy.SparkSubmit\$.main(SparkSubmit.scala:121)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
``````

Line 51 refers to the line
`var g2 = g.mapVertices(`

Line 85 refers to the line
`val results = dijkstra(my_graph, 1L).vertices.map(_._2).collect`

What method is this exception referring to? I am able to package with
`sbt`
without error and I canno see what method I am calling whcih does not exist.

The issue wasn't in a version error nor a missing implementation but a misleading error from the compiler.

Ok so here is the thing: After investigating the code, I have noticed that the following section contained one extra closing parentheses :

``````val currentVertexId: VertexId = 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
^
|
``````

You'll just need to remove that extra parentheses and it will work perfectly. Here is the full code :

``````// scala> :pa
// Entering paste mode (ctrl-D to finish)

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 else Double.MaxValue, List[VertexId]())
)

for (i <- 1L to g.vertices.count - 1) {
val currentVertexId: VertexId = 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: VertexRDD[(Double, List[VertexId])] =
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
))
}

//  Path Finding - random node from which to find all paths
``````

Now, let's test it :

``````val myVertices: RDD[(VertexId, String)] = sc.makeRDD(Array((1L, "A"), (2L, "B"), (3L, "C"), (4L, "D"), (5L, "E"), (6L, "F"), (7L, "G")))
val myEdges: RDD[Edge[Double]] = sc.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 my_graph = Graph(myVertices, myEdges).cache()

val v1 = 4000000028222916L

val results = dijkstra(my_graph, 1L).vertices.map(_._2).collect

// [CTRL-D]
// Exiting paste mode, now interpreting.
// [Lscala.Tuple2;@668a0785
// import org.apache.spark.graphx._
// myVertices: org.apache.spark.rdd.RDD[(org.apache.spark.graphx.VertexId, String)] = ParallelCollectionRDD[556] at makeRDD at <console>:37
// myEdges: org.apache.spark.rdd.RDD[org.apache.spark.graphx.Edge[Double]] = ParallelCollectionRDD[557] at makeRDD at <console>:39
// my_graph: org.apache.spark.graphx.Graph[String,Double] = org.apache.spark.graphx.impl.GraphImpl@49ea0d90
// dijkstra: [VD](g: org.apache.spark.graphx.Graph[VD,Double], origin: org.apache.spark.graphx.VertexId)org.apache.spark.graphx.Graph[(VD, List[Any]),Double]
// v1: Long = 4000000028222916
// results: Array[(String, List[Any])] = Array((A,List(0.0, List())), (B,List(7.0, List(1))), (C,List(15.0, Li...
scala> results.foreach(println)
// (A,List(0.0, List()))
// (B,List(7.0, List(1)))
// (C,List(15.0, List(1, 2)))
// (D,List(5.0, List(1)))
// (E,List(14.0, List(1, 2)))
// (F,List(11.0, List(1, 4)))
// (G,List(22.0, List(1, 4, 6)))
``````
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