我有代码可以在聚类后计算平方误差的集合和内，而我主要从Spark mllib源代码中获取代码。
当我使用spark API运行类似代码时，它将在许多不同的（分布式）作业中运行并成功运行。当我运行它的代码（应该与Spark代码做同样的事情）时，我得到了堆栈溢出错误。有什么想法吗？
这是代码：
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)
我相信我正在使用所有相同的并行化作业作为spark，但是对我来说不起作用。关于分发/帮助我的代码的任何建议，看看为什么我的代码中会发生内存溢出，这将非常有帮助
这是一个非常类似spark的源代码链接：
KMeansModel和KMeans
这是运行良好的代码：
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)
错误输出如下：
org.apache.spark.SparkException：由于阶段失败而导致作业中止：94.0阶段中的任务1失败了4次，最近一次失败：94.0阶段中的任务1.3丢失（TID 37663，ip-172-31-13-209.ec2。内部）：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 $$ iwC $$ 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 $$ iwC $ iwC $ iwC $ .dot（：226）
...
然后下来：
驱动程序堆栈跟踪：org.apache.spark.scheduler.DAGScheduler $$ anonfun $ abortStage $ 1在org.apache.spark.scheduler.DAGScheduler.org $ apache $ spark $ scheduler $ DAGScheduler $$ failJobAndIndependentStages（DAGScheduler.scala：1431）处。在org.apache.spark.scheduler.DAGScheduler $$ anonfun $ abortStage $ 1.apply（DAGScheduler.scala：1418）处应用（DAGScheduler.scala：1419）在scala.collection.mutable.ResizableArray $ class.foreach（ResizableArray.scala： 59）在org.apache.spark.scheduler.DAGScheduler.abortStage（DAGScheduler.scala：1418）的scala.collection.mutable.ArrayBuffer.foreach（ArrayBuffer.scala：47）在org.apache.spark.scheduler.DAGScheduler $$ org.apache.spark.scheduler.DAGScheduler $$ anonfun $ handleTaskSetFailed $ 1.apply（DAGScheduler.scala：799）上的anonfun $ handleTaskSetFailed $ 1.apply（DAGScheduler.scala：799）在scala.Option.foreach（Option.scala：236） ），位于org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive（DAGScheduler.org）上的org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed（DAGScheduler.scala：799）。 scala：1640）位于org.apache.spark.org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive（DAGScheduler.scala：1599），org.org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive（DAGScheduler.scala：1588）位于org.apache.spark。 org.apache.spark.scheduler.DAGScheduler.runJob（DAGScheduler.scala：620）上的.EventLoop $$ anon $ 1.run（EventLoop.scala：48）在org.apache.spark.SparkContext.runJob（SparkContext.scala：1832） ）的org.apache.spark.SparkContext.runJob（SparkContext.scala：1952）的org.apache.spark.rdd.RDD $$ anonfun $ fold $ 1.apply（RDD.scala：1088）的org.apache.spark。 org.apache.spark.rdd.RDDOperationScope $ .withScope（RDDOperationScope.scala：111）的rdd.RDDOperationScope $ .withScope（RDDOperationScope.scala：150）在org.apache.spark.rdd.RDD.withScope（RDD.scala）： 316），位于org.apache.spark.rdd.RDD.fold（RDD.scala：1082），位于org.apache.spark.rdd.DoubleRDDFunctions $$ anonfun $ sum $ 1.apply $ mcD $ sp（DoubleRDDFunctions.scala：34）在org.apache.spark.rdd.DoubleRDDFunctions $$ anonfun $ sum $ 1.apply（DoubleRDDFunctions.scala：34）在o rg.apache.spark.rdd.DoubleRDDFunctions $$ anonfun $ sum $ 1.apply（DoubleRDDFunctions.scala：34）在org.apache.spark.rdd.RDDOperationScope $ .withScope（RDDOperationScope.scala：150）在org.apache.spark位于org.apache.spark.rdd.RDD.RDD.withScope（RDD.scala：316）的.rdd.RDDOperationScope $ .withScope（RDDOperationScope.scala：111），位于org.apache.spark.rdd.DoubleRDDFunctions.sum（DoubleRDDFunctions.scala： 33）
最佳答案
似乎很简单：正在发生什么：您在这里递归调用dot方法：
def dot(x: Vector, y: Vector): Double = {
require(x.size == y.size,
"BLAS.dot(x: Vector, y:Vector) was given Vectors with non-matching sizes:" +
" x.size = " + x.size + ", y.size = " + y.size)
(x, y) match {
case (dx: DenseVector, dy: DenseVector) =>
dot(dx, dy)
case (sx: SparseVector, dy: DenseVector) =>
dot(sx, dy)
case (dx: DenseVector, sy: SparseVector) =>
dot(sy, dx)
case (sx: SparseVector, sy: SparseVector) =>
dot(sx, sy)
case _ =>
throw new IllegalArgumentException(s"dot doesn't support (${x.getClass}, ${y.getClass}).")
}
}
后续对
dot的递归调用与前者具有相同的参数-因此，递归永远不会得出结论。stacktrace也会告诉您-注意位置在点方法中：
$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $ iwC $$ iwC $$ iwC $$ iwC $ iwC $$ iwC $$$$$ c57ec8bf9b0d5f6161b97741d596ff0 $$ w $$ iwC $$ iwC $ iwC $ iwC $ iwC $$ iwC $ iwC $ iwC $ iwC $ iwC $$ iwC $ iwC $ iwC $ iwC $ iwC $$ iwC.dot （：226）在