[SPARK-27406] UnsafeArrayData serialization breaks when two machines have different Oops size

• Type: Bug
• Status: Resolved
• Priority: Major
• Resolution: Fixed
• Affects Version/s: 2.4.0, 2.4.1
• Fix Version/s: 2.4.2, 3.0.0
• Component/s: SQL
• Labels: correctness
• Assignee: peng bo
• Reporter: peng bo
• Created: 08/Apr/19 05:52
• Updated: 02/Mar/20 21:02
• Resolved: 10/Apr/19 09:07

Description

ApproxCountDistinctForIntervals holds the UnsafeArrayData data to initialize endpoints. When the UnsafeArrayData is serialized with Java serialization, the BYTE_ARRAY_OFFSET in memory can change if two machines have different pointer width (Oops in JVM).

It's similar to SPARK-10914.

java.lang.NullPointerException
	at org.apache.spark.sql.catalyst.expressions.aggregate.ApproxCountDistinctForIntervals$$anonfun$endpoints$1.apply(ApproxCountDistinctForIntervals.scala:69)
	at org.apache.spark.sql.catalyst.expressions.aggregate.ApproxCountDistinctForIntervals$$anonfun$endpoints$1.apply(ApproxCountDistinctForIntervals.scala:69)
	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$ofRef.map(ArrayOps.scala:186)
	at org.apache.spark.sql.catalyst.expressions.aggregate.ApproxCountDistinctForIntervals.endpoints$lzycompute(ApproxCountDistinctForIntervals.scala:69)
	at org.apache.spark.sql.catalyst.expressions.aggregate.ApproxCountDistinctForIntervals.endpoints(ApproxCountDistinctForIntervals.scala:66)
	at org.apache.spark.sql.catalyst.expressions.aggregate.ApproxCountDistinctForIntervals.org$apache$spark$sql$catalyst$expressions$aggregate$ApproxCountDistinctForIntervals$$hllppArray$lzycompute(ApproxCountDistinctForIntervals.scala:94)
	at org.apache.spark.sql.catalyst.expressions.aggregate.ApproxCountDistinctForIntervals.org$apache$spark$sql$catalyst$expressions$aggregate$ApproxCountDistinctForIntervals$$hllppArray(ApproxCountDistinctForIntervals.scala:93)
	at org.apache.spark.sql.catalyst.expressions.aggregate.ApproxCountDistinctForIntervals.org$apache$spark$sql$catalyst$expressions$aggregate$ApproxCountDistinctForIntervals$$numWordsPerHllpp$lzycompute(ApproxCountDistinctForIntervals.scala:104)
	at org.apache.spark.sql.catalyst.expressions.aggregate.ApproxCountDistinctForIntervals.org$apache$spark$sql$catalyst$expressions$aggregate$ApproxCountDistinctForIntervals$$numWordsPerHllpp(ApproxCountDistinctForIntervals.scala:104)
	at org.apache.spark.sql.catalyst.expressions.aggregate.ApproxCountDistinctForIntervals.totalNumWords$lzycompute(ApproxCountDistinctForIntervals.scala:106)
	at org.apache.spark.sql.catalyst.expressions.aggregate.ApproxCountDistinctForIntervals.totalNumWords(ApproxCountDistinctForIntervals.scala:106)
	at org.apache.spark.sql.catalyst.expressions.aggregate.ApproxCountDistinctForIntervals.createAggregationBuffer(ApproxCountDistinctForIntervals.scala:110)
	at org.apache.spark.sql.catalyst.expressions.aggregate.ApproxCountDistinctForIntervals.createAggregationBuffer(ApproxCountDistinctForIntervals.scala:44)
	at org.apache.spark.sql.catalyst.expressions.aggregate.TypedImperativeAggregate.initialize(interfaces.scala:528)
	at org.apache.spark.sql.execution.aggregate.ObjectAggregationIterator$$anonfun$initAggregationBuffer$2.apply(ObjectAggregationIterator.scala:120)
	at org.apache.spark.sql.execution.aggregate.ObjectAggregationIterator$$anonfun$initAggregationBuffer$2.apply(ObjectAggregationIterator.scala:120)
	at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
	at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
	at org.apache.spark.sql.execution.aggregate.ObjectAggregationIterator.initAggregationBuffer(ObjectAggregationIterator.scala:120)
	at org.apache.spark.sql.execution.aggregate.ObjectAggregationIterator.org$apache$spark$sql$execution$aggregate$ObjectAggregationIterator$$createNewAggregationBuffer(ObjectAggregationIterator.scala:112)
	at org.apache.spark.sql.execution.aggregate.ObjectAggregationIterator.getAggregationBufferByKey(ObjectAggregationIterator.scala:128)
	at org.apache.spark.sql.execution.aggregate.ObjectAggregationIterator.processInputs(ObjectAggregationIterator.scala:150)
	at org.apache.spark.sql.execution.aggregate.ObjectAggregationIterator.<init>(ObjectAggregationIterator.scala:78)
	at org.apache.spark.sql.execution.aggregate.ObjectHashAggregateExec$$anonfun$doExecute$1$$anonfun$2.apply(ObjectHashAggregateExec.scala:114)
	at org.apache.spark.sql.execution.aggregate.ObjectHashAggregateExec$$anonfun$doExecute$1$$anonfun$2.apply(ObjectHashAggregateExec.scala:105)
	at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndexInternal$1$$anonfun$12.apply(RDD.scala:823)
	at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndexInternal$1$$anonfun$12.apply(RDD.scala:823)
	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
	at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)
	at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:55)
	at org.apache.spark.scheduler.Task.run(Task.scala:121)

Issue Links

• relates to: SPARK-10914 UnsafeRow serialization breaks when two machines have different Oops size
• links to: GitHub Pull Request #24317