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https://issues.apache.org/jira/browse/SPARK-27406?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Dongjoon Hyun updated SPARK-27406:
----------------------------------
Affects Version/s: 2.4.0
> 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: 2.4.0, 2.4.1
> Reporter: peng bo
> Assignee: peng bo
> Priority: Major
> Labels: correctness
> Fix For: 2.4.2, 3.0.0
>
>
> 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.
> {code:java}
> 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)
> {code}
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