[SPARK-15062] Show on DataFrame causes OutOfMemoryError, NegativeArraySizeException or segfault

• Type: Bug
• Status: Resolved
• Priority: Blocker
• Resolution: Fixed
• Fix Version/s: 2.0.0
• Component/s: SQL

Environment

spark-2.0.0-SNAPSHOT using commit hash 90787de864b58a1079c23e6581381ca8ffe7685f and Java 1.7.0_67

• Target Version/s: 2.0.0
• Assignee: Bo Meng
• Reporter: koert kuipers
• Created: 02/May/16 14:14
• Updated: 06/Oct/16 19:01
• Resolved: 03/May/16 01:20

Description

scala> val dfComplicated = sc.parallelize(List((Map("1" -> "a"), List("b", "c")), (Map("2" -> "b"), List("d", "e")))).toDF
...
dfComplicated: org.apache.spark.sql.DataFrame = [_1: map<string,string>, _2: array<string>]

scala> dfComplicated.show
java.lang.OutOfMemoryError: Java heap space
  at org.apache.spark.unsafe.types.UTF8String.getBytes(UTF8String.java:229)
  at org.apache.spark.unsafe.types.UTF8String.toString(UTF8String.java:821)
  at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificSafeProjection.apply(Unknown Source)
  at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.fromRow(ExpressionEncoder.scala:241)
  at org.apache.spark.sql.Dataset$$anonfun$org$apache$spark$sql$Dataset$$execute$1$1$$anonfun$apply$13.apply(Dataset.scala:2121)
  at org.apache.spark.sql.Dataset$$anonfun$org$apache$spark$sql$Dataset$$execute$1$1$$anonfun$apply$13.apply(Dataset.scala:2121)
  at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
  at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
  at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
  at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
  at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
  at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:186)
  at org.apache.spark.sql.Dataset$$anonfun$org$apache$spark$sql$Dataset$$execute$1$1.apply(Dataset.scala:2121)
  at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:54)
  at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2408)
  at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$execute$1(Dataset.scala:2120)
  at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collect(Dataset.scala:2127)
  at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:1861)
  at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:1860)
  at org.apache.spark.sql.Dataset.withTypedCallback(Dataset.scala:2438)
  at org.apache.spark.sql.Dataset.head(Dataset.scala:1860)
  at org.apache.spark.sql.Dataset.take(Dataset.scala:2077)
  at org.apache.spark.sql.Dataset.showString(Dataset.scala:238)
  at org.apache.spark.sql.Dataset.show(Dataset.scala:529)
  at org.apache.spark.sql.Dataset.show(Dataset.scala:489)
  at org.apache.spark.sql.Dataset.show(Dataset.scala:498)
  ... 6 elided

scala>

By increasing memory to 8G one will instead get a NegativeArraySizeException or a segfault.

See here for original discussion:
http://apache-spark-developers-list.1001551.n3.nabble.com/spark-2-segfault-td17381.html

Issue Links

• links to: [Github] Pull Request #12849 (bomeng)
• links to: [Github] Pull Request #15380 (brkyvz)