我有一段编程代码，用于计算聚类后的集合平方误差和，这段代码大部分是从Spark mllib源代码中获取的。
当我使用Spark API运行类似的代码时，它会在许多不同的（分布式）作业中运行，并成功完成。但是当我运行自己的代码（应该与Spark代码执行相同的操作）时，我会遇到堆栈溢出错误。有什么想法吗？
以下是代码：
import java.util.Arrays
import org.apache.spark.mllib.linalg.{Vectors, Vector}
import org.apache.spark.mllib.linalg._
import org.apache.spark.mllib.linalg.distributed.RowMatrix
import org.apache.spark.rdd.RDD
import org.apache.spark.api.java.JavaRDD
import breeze.linalg.{axpy => brzAxpy, inv, svd => brzSvd, DenseMatrix => BDM, DenseVector => BDV,
MatrixSingularException, SparseVector => BSV, CSCMatrix => BSM, Matrix => BM}
val EPSILON = {
var eps = 1.0
while ((1.0 + (eps / 2.0)) != 1.0) {
eps /= 2.0
}
eps
}
def dot(x: Vector, y: Vector): Double = {
require(x.size == y.size,
"BLAS.dot(x: Vector, y:Vector) was given Vectors with non-matching sizes:" +
" x.size = " + x.size + ", y.size = " + y.size)
(x, y) match {
case (dx: DenseVector, dy: DenseVector) =>
dot(dx, dy)
case (sx: SparseVector, dy: DenseVector) =>
dot(sx, dy)
case (dx: DenseVector, sy: SparseVector) =>
dot(sy, dx)
case (sx: SparseVector, sy: SparseVector) =>
dot(sx, sy)
case _ =>
throw new IllegalArgumentException(s"dot doesn't support (${x.getClass}, ${y.getClass}).")
}
}
def fastSquaredDistance(
v1: Vector,
norm1: Double,
v2: Vector,
norm2: Double,
precision: Double = 1e-6): Double = {
val n = v1.size
require(v2.size == n)
require(norm1 >= 0.0 && norm2 >= 0.0)
val sumSquaredNorm = norm1 * norm1 + norm2 * norm2
val normDiff = norm1 - norm2
var sqDist = 0.0
/*
* The relative error is
* <pre>
* EPSILON * ( \|a\|_2^2 + \|b\\_2^2 + 2 |a^T b|) / ( \|a - b\|_2^2 ),
* </pre>
* which is bounded by
* <pre>
* 2.0 * EPSILON * ( \|a\|_2^2 + \|b\|_2^2 ) / ( (\|a\|_2 - \|b\|_2)^2 ).
* </pre>
* The bound doesn't need the inner product, so we can use it as a sufficient condition to
* check quickly whether the inner product approach is accurate.
*/
val precisionBound1 = 2.0 * EPSILON * sumSquaredNorm / (normDiff * normDiff + EPSILON)
if (precisionBound1 < precision) {
sqDist = sumSquaredNorm - 2.0 * dot(v1, v2)
} else if (v1.isInstanceOf[SparseVector] || v2.isInstanceOf[SparseVector]) {
val dotValue = dot(v1, v2)
sqDist = math.max(sumSquaredNorm - 2.0 * dotValue, 0.0)
val precisionBound2 = EPSILON * (sumSquaredNorm + 2.0 * math.abs(dotValue)) /
(sqDist + EPSILON)
if (precisionBound2 > precision) {
sqDist = Vectors.sqdist(v1, v2)
}
} else {
sqDist = Vectors.sqdist(v1, v2)
}
sqDist
}
def findClosest(
centers: TraversableOnce[Vector],
point: Vector): (Int, Double) = {
var bestDistance = Double.PositiveInfinity
var bestIndex = 0
var i = 0
centers.foreach { center =>
// Since `\|a - b\| \geq |\|a\| - \|b\||`, we can use this lower bound to avoid unnecessary
// distance computation.
var lowerBoundOfSqDist = Vectors.norm(center, 2.0) - Vectors.norm(point, 2.0)
lowerBoundOfSqDist = lowerBoundOfSqDist * lowerBoundOfSqDist
if (lowerBoundOfSqDist < bestDistance) {
val distance: Double = fastSquaredDistance(center, Vectors.norm(center, 2.0), point, Vectors.norm(point, 2.0))
if (distance < bestDistance) {
bestDistance = distance
bestIndex = i
}
}
i += 1
}
(bestIndex, bestDistance)
}
def pointCost(
centers: TraversableOnce[Vector],
point: Vector): Double =
findClosest(centers, point)._2
def clusterCentersIter: Iterable[Vector] =
clusterCenters.map(p => p)
def computeCostZep(indata: RDD[Vector]): Double = {
val bcCenters = indata.context.broadcast(clusterCenters)
indata.map(p => pointCost(bcCenters.value, p)).sum()
}
computeCostZep(projectedData)
我相信我正在使用与Spark相同的所有并行化作业，但它对我没有用。任何关于使我的代码分布式/帮助查看为什么内存溢出发生在我的代码中的建议都将非常有帮助。
这是一个与Spark非常相似的源代码链接：KMeansModel和KMeans
这是可以正常运行的代码：
val clusters = KMeans.train(projectedData, numClusters, numIterations)
val clusterCenters = clusters.clusterCenters
// Evaluate clustering by computing Within Set Sum of Squared Errors
val WSSSE = clusters.computeCost(projectedData)
println("Within Set Sum of Squared Errors = " + WSSSE)
这是错误输出：
org.apache.spark.SparkException：由于阶段失败而中止作业：第94.0阶段中的任务1失败了4次，最近的失败是：在第94.0阶段中丢失任务1.3（TID 37663，ip-172-31-13-209.ec2.internal）：java.lang.StackOverflowError at $ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$$$ $ c57ec8bf9b0d5f6161b97741d596ff0 $ $$$ wC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC.dot（：226）at $ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$$$ $ c57ec8bf9b0d5f6161b97741d596ff0 $ $$$ wC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC.dot（：226）
之后：
驱动程序堆栈跟踪：在org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431)处失败作业和独立阶段，在org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)处中止阶段，在scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)处进行可变数组遍历， 在org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418)处中止阶段， 在org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)处处理任务集失败， 在org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)处接收到DAGScheduler事件处理循环的消息， 在org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)处运行事件循环， 在org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)处运行作业，在org.apache.spark.SparkContext.runJob(SparkContext.scala:1832)处运行SparkContext作业，在org.apache.spark.rdd.RDD$$anonfun$fold$1.apply(RDD.scala:1088)处应用RDD fold操作，在org.apache.spark.rdd.DoubleRDDFunctions$$anonfun$sum$1.apply$mcD$sp(DoubleRDDFunctions.scala:34)处应用DoubleRDDFunctions sum操作