[
https://issues.apache.org/jira/browse/SPARK-16071?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Xiangrui Meng updated SPARK-16071:
----------------------------------
Description:
Several bugs have been found caused by integer overflows in Tungsten. This JIRA
is for taking a final pass before 2.0 release to reduce potential bugs and
issues. We should do at least the following:
* Raise exception early instead of NegativeArraySize
* Document clearly the largest array size we support in DataFrames.
To reproduce one of the issues:
{code}
val n = 1e8.toInt // try 2e8, 3e8
sc.parallelize(0 until 1, 1).map(i => new Array[Int](n)).toDS.map(_.size).show()
{code}
Result:
* n=1e8: correct but with slow (see SPARK-16043)
* n=2e8: NegativeArraySize exception
{code:none}
java.lang.NegativeArraySizeException
at
org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder.grow(BufferHolder.java:61)
at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown
Source)
at
org.apache.spark.sql.execution.RDDScanExec$$anonfun$doExecute$1$$anonfun$apply$3.apply(ExistingRDD.scala:123)
at
org.apache.spark.sql.execution.RDDScanExec$$anonfun$doExecute$1$$anonfun$apply$3.apply(ExistingRDD.scala:121)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown
Source)
at
org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at
org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:370)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:246)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:240)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:780)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:780)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
at org.apache.spark.scheduler.Task.run(Task.scala:85)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
{code}
* n=3e8: NegativeArraySize exception but at a different location
{code:none}
java.lang.RuntimeException: Error while encoding:
java.lang.NegativeArraySizeException
newInstance(class org.apache.spark.sql.catalyst.util.GenericArrayData) AS
value#108
+- newInstance(class org.apache.spark.sql.catalyst.util.GenericArrayData)
+- input[0, [I, true]
at
org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.toRow(ExpressionEncoder.scala:257)
at
org.apache.spark.sql.SparkSession$$anonfun$4.apply(SparkSession.scala:430)
at
org.apache.spark.sql.SparkSession$$anonfun$4.apply(SparkSession.scala:430)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown
Source)
at
org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at
org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:370)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:246)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:240)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:780)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:780)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
at org.apache.spark.scheduler.Task.run(Task.scala:85)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
{code}
was:
Several bugs have been found caused by integer overflows in Tungsten. This JIRA
is for taking a final pass before 2.0 release to reduce potential bugs and
issues. We should do at least the following:
* Raise exception early instead of NegativeArraySize
* Document clearly the largest array size we support in DataFrames.
To reproduce one of the issues:
{code}
val n = 1e8.toInt // try 2e8, 3e8
sc.parallelize(0 until 1, 1).map(i => new Array[Int](n)).toDS.map(_.size).show()
{code}
Result:
* n=1e8: correct but with slow
* n=2e8: NegativeArraySize exception
{code:none}
java.lang.NegativeArraySizeException
at
org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder.grow(BufferHolder.java:61)
at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown
Source)
at
org.apache.spark.sql.execution.RDDScanExec$$anonfun$doExecute$1$$anonfun$apply$3.apply(ExistingRDD.scala:123)
at
org.apache.spark.sql.execution.RDDScanExec$$anonfun$doExecute$1$$anonfun$apply$3.apply(ExistingRDD.scala:121)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown
Source)
at
org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at
org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:370)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:246)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:240)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:780)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:780)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
at org.apache.spark.scheduler.Task.run(Task.scala:85)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
{code}
* n=3e8: NegativeArraySize exception but at a different location
{code:none}
java.lang.RuntimeException: Error while encoding:
java.lang.NegativeArraySizeException
newInstance(class org.apache.spark.sql.catalyst.util.GenericArrayData) AS
value#108
+- newInstance(class org.apache.spark.sql.catalyst.util.GenericArrayData)
+- input[0, [I, true]
at
org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.toRow(ExpressionEncoder.scala:257)
at
org.apache.spark.sql.SparkSession$$anonfun$4.apply(SparkSession.scala:430)
at
org.apache.spark.sql.SparkSession$$anonfun$4.apply(SparkSession.scala:430)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown
Source)
at
org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at
org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:370)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:246)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:240)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:780)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:780)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
at org.apache.spark.scheduler.Task.run(Task.scala:85)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
{code}
> Not sufficient array size checks to avoid integer overflows in Tungsten
> -----------------------------------------------------------------------
>
> Key: SPARK-16071
> URL: https://issues.apache.org/jira/browse/SPARK-16071
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.0.0
> Reporter: Xiangrui Meng
> Assignee: Yin Huai
> Priority: Critical
>
> Several bugs have been found caused by integer overflows in Tungsten. This
> JIRA is for taking a final pass before 2.0 release to reduce potential bugs
> and issues. We should do at least the following:
> * Raise exception early instead of NegativeArraySize
> * Document clearly the largest array size we support in DataFrames.
> To reproduce one of the issues:
> {code}
> val n = 1e8.toInt // try 2e8, 3e8
> sc.parallelize(0 until 1, 1).map(i => new
> Array[Int](n)).toDS.map(_.size).show()
> {code}
> Result:
> * n=1e8: correct but with slow (see SPARK-16043)
> * n=2e8: NegativeArraySize exception
> {code:none}
> java.lang.NegativeArraySizeException
> at
> org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder.grow(BufferHolder.java:61)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown
> Source)
> at
> org.apache.spark.sql.execution.RDDScanExec$$anonfun$doExecute$1$$anonfun$apply$3.apply(ExistingRDD.scala:123)
> at
> org.apache.spark.sql.execution.RDDScanExec$$anonfun$doExecute$1$$anonfun$apply$3.apply(ExistingRDD.scala:121)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown
> Source)
> at
> org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at
> org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:370)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:246)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:240)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:780)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:780)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
> at org.apache.spark.scheduler.Task.run(Task.scala:85)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:745)
> {code}
> * n=3e8: NegativeArraySize exception but at a different location
> {code:none}
> java.lang.RuntimeException: Error while encoding:
> java.lang.NegativeArraySizeException
> newInstance(class org.apache.spark.sql.catalyst.util.GenericArrayData) AS
> value#108
> +- newInstance(class org.apache.spark.sql.catalyst.util.GenericArrayData)
> +- input[0, [I, true]
> at
> org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.toRow(ExpressionEncoder.scala:257)
> at
> org.apache.spark.sql.SparkSession$$anonfun$4.apply(SparkSession.scala:430)
> at
> org.apache.spark.sql.SparkSession$$anonfun$4.apply(SparkSession.scala:430)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown
> Source)
> at
> org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at
> org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:370)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:246)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:240)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:780)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:780)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
> at org.apache.spark.scheduler.Task.run(Task.scala:85)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:745)
> {code}
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