[jira] [Created] (SPARK-27406) UnsafeArrayData serialization breaks when two machines have different Oops size

["peng bo (JIRA)" <ji...@apache.org>]

peng bo created SPARK-27406:
-------------------------------

             Summary: UnsafeArrayData serialization breaks when two machines have different Oops size
                 Key: SPARK-27406
                 URL: https://issues.apache.org/jira/browse/SPARK-27406
             Project: Spark
          Issue Type: Bug
          Components: SQL
    Affects Versions: 2.4.1
            Reporter: peng bo


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)


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.



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