【发布时间】：2016-09-27 10:58:30
【问题描述】：
我有代码在聚类后计算平方误差的集合内，我主要从 Spark mllib 源代码中获取。
当我使用 spark API 运行类似代码时，它在许多不同的（分布式）作业中运行并成功运行。当我运行它时，我的代码（应该与 Spark 代码做同样的事情）我得到一个堆栈溢出错误。任何想法为什么？
代码如下：
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，但它对我不起作用。任何关于使我的代码分发/帮助了解为什么我的代码中会发生内存溢出的建议都会非常有帮助
这里是 spark 源代码的链接，非常相似： 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)
这是错误输出：
org.apache.spark.SparkException：作业因阶段故障而中止：阶段 94.0 中的任务 1 失败 4 次，最近一次失败：阶段 94.0 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$$iwC.dot(:226) at $iwC$$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 $$iwC.dot(:226) ...
及以后：
驱动程序堆栈跟踪：在 org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431) 在 org.apache.spark.scheduler.DAGScheduler$$anonfun$ abortStage$1.apply(DAGScheduler.scala:1419) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray .scala:59) 在 scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) 在 org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418) 在 org.apache.spark.scheduler。 DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799) at scala.Option.foreach(Option. scala:236) 在 org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799) 在 org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGSchedul er.scala:1640) 在 org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599) 在 org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588) 在 org.apache.spark .util.EventLoop$$anon$1.run(EventLoop.scala:48) at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620) at org.apache.spark.SparkContext.runJob(SparkContext.scala :1832) 在 org.apache.spark.SparkContext.runJob(SparkContext.scala:1952) 在 org.apache.spark.rdd.RDD$$anonfun$fold$1.apply(RDD.scala:1088) 在 org.apache。 spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150) 在 org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111) 在 org.apache.spark.rdd.RDD.withScope(RDD. scala:316) at org.apache.spark.rdd.RDD.fold(RDD.scala:1082) at org.apache.spark.rdd.DoubleRDDFunctions$$anonfun$sum$1.apply$mcD$sp(DoubleRDDFunctions.scala: 34) 在 org.apache.spark.rdd.DoubleRDDFunctions$$anonfun$sum$1.apply(DoubleRDDFunctions.scala:34) t org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150) 在 org.apache.spark.rdd.org.apache.spark.rdd.DoubleRDDFunctions$$anonfun$sum$1.apply(DoubleRDDFunctions.scala:34)。 spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111) at org.apache.spark.rdd.RDD.withScope(RDD.scala:316) at org.apache.spark.rdd.DoubleRDDFunctions.sum(DoubleRDDFunctions.scala :33)
【问题讨论】：
-
我刚刚编辑了我原来的问题。它显示了我使用运行没有问题的 KMeansModel.computeCost 方法运行的代码。当我说我的时，我的意思是我在上面发布的代码。
-
你从哪里得到堆栈溢出？
-
RDD 是 Spark 并行/分布式抽象/数据结构。 Vectors 和 DenseVectors 是简单的局部向量数据结构。如果你想要并行性，你应该将它们包装在 RDD 中。
-
@Brian 我不确定。我发布了错误输出，但我不确定如何从中判断溢出发生的位置。我只确定它是在编译代码之后发生的。
-
@EhsanM.Kermani 我不这样做吗？代码中向量未包含在 RDD 中的唯一位置是在 pointCost 和 findClosest 中，并且该向量列表的长度为 5（它是 KMeans 找到的质心，设置为 5）
标签： scala apache-spark apache-spark-mllib