java - Apache Spark : StackOverflowError when trying to indexing string columns

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)

并希望将它们编入索引。为此，我为每个字符串列创建了索引器

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

并创建管道

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

但是当我尝试使用此管道转换我的初始数据框时

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

我收到 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)
...

我做错了什么？谢谢。

Answer 1

很可能没有足够的内存来保存所有堆栈帧。我在训练 RandomForestModel 时经历过类似的事情。对我有用的解决方法是使用附加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'