Apache Spark:尝试索引字符串列时出现StackOverflowError

Question

我有大约5000行和950列的csv文件.首先我将它加载到DataFrame:

val data = sqlContext.read
  .format(csvFormat)
  .option("header", "true")
  .option("inferSchema", "true")
  .load(file)
  .cache()

之后我搜索所有字符串列

val featuresToIndex = data.schema
  .filter(_.dataType == StringType)
  .map(field => field.name)

并想要索引它们.为此,我为每个字符串列创建索引器

val stringIndexers = featuresToIndex.map(colName =>
  new StringIndexer()
    .setInputCol(colName)
    .setOutputCol(colName + "Indexed"))

并创建管道

val pipeline = new Pipeline().setStages(stringIndexers.toArray)

但是当我尝试用这个管道转换我的初始数据帧时

val indexedDf = pipeline.fit(data).transform(data)

我得到StackOverflowError

16/07/05 16:55:12 INFO DAGScheduler: Job 4 finished: countByValue at StringIndexer.scala:86, took 7.882774 s
Exception in thread "main" java.lang.StackOverflowError
at scala.collection.immutable.Set$Set1.contains(Set.scala:84)
at scala.collection.immutable.Set$Set1.$plus(Set.scala:86)
at scala.collection.immutable.Set$Set1.$plus(Set.scala:81)
at scala.collection.mutable.SetBuilder.$plus$eq(SetBuilder.scala:22)
at scala.collection.mutable.SetBuilder.$plus$eq(SetBuilder.scala:20)
at scala.collection.generic.Growable$class.loop$1(Growable.scala:53)
at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:57)
at scala.collection.mutable.SetBuilder.$plus$plus$eq(SetBuilder.scala:20)
at scala.collection.TraversableLike$class.to(TraversableLike.scala:590)
at scala.collection.AbstractTraversable.to(Traversable.scala:104)
at scala.collection.TraversableOnce$class.toSet(TraversableOnce.scala:304)
at scala.collection.AbstractTraversable.toSet(Traversable.scala:104)
at org.apache.spark.sql.catalyst.trees.TreeNode.containsChild$lzycompute(TreeNode.scala:86)
at org.apache.spark.sql.catalyst.trees.TreeNode.containsChild(TreeNode.scala:86)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:280)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
...

我究竟做错了什么？谢谢.

Answer 1

最有可能的是没有足够的内存来保留所有堆栈帧.我在训练的RandomForestModel时遇到类似的东西.适用于我的解决方法是使用其他参数运行我的驱动程序应用程序(即Web服务):

-XX:ThreadStackSize=81920 -Dspark.executor.extraJavaOptions='-XX:ThreadStackSize=81920'

Answer 2

看来我找到了那种解决方案 - 使用spark 2.0。以前，我使用的是 1.6.2 - 这是发布时的最新版本。我尝试使用2.0的预览版，但也有问题重现。