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Posted to issues@spark.apache.org by "Hyukjin Kwon (Jira)" <ji...@apache.org> on 2022/08/05 01:43:00 UTC
[jira] [Commented] (SPARK-39981) CheckOverflowInTableInsert returns exception rather than throwing it
[ https://issues.apache.org/jira/browse/SPARK-39981?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17575508#comment-17575508 ]
Hyukjin Kwon commented on SPARK-39981:
--------------------------------------
will make a quick fix soon. Thanks for reporting this.
> CheckOverflowInTableInsert returns exception rather than throwing it
> --------------------------------------------------------------------
>
> Key: SPARK-39981
> URL: https://issues.apache.org/jira/browse/SPARK-39981
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 3.4.0, 3.3.1
> Reporter: Jason Darrell Lowe
> Priority: Major
>
> CheckOverflowInTableInsert added by [SPARK-39865] returns the result of QueryExecutionErrors.castingCauseOverflowErrorInTableInsert rather than throwing it. This results in a ClassCastException at runtime, since it cannot interpret the exception object as the expected value type.
> For example:
> {noformat}
> scala> sql("create table tiny(i tinyint)")
> 22/08/04 10:27:32 WARN ResolveSessionCatalog: A Hive serde table will be created as there is no table provider specified. You can set spark.sql.legacy.createHiveTableByDefault to false so that native data source table will be created instead.
> res0: org.apache.spark.sql.DataFrame = []
> scala> sql("insert into tiny values (1000);")
> java.lang.ClassCastException: org.apache.spark.SparkArithmeticException cannot be cast to java.lang.Byte
> at scala.runtime.BoxesRunTime.unboxToByte(BoxesRunTime.java:95)
> at org.apache.spark.sql.catalyst.InternalRow$.$anonfun$getWriter$2(InternalRow.scala:171)
> at org.apache.spark.sql.catalyst.InternalRow$.$anonfun$getWriter$2$adapted(InternalRow.scala:171)
> at org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection.$anonfun$fieldWriters$2(InterpretedMutableProjection.scala:76)
> at org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection.$anonfun$fieldWriters$2$adapted(InterpretedMutableProjection.scala:76)
> at org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection.apply(InterpretedMutableProjection.scala:103)
> at org.apache.spark.sql.catalyst.optimizer.ConvertToLocalRelation$$anonfun$apply$46.$anonfun$applyOrElse$75(Optimizer.scala:1994)
> at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:286)
> at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
> at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
> at scala.collection.TraversableLike.map(TraversableLike.scala:286)
> at scala.collection.TraversableLike.map$(TraversableLike.scala:279)
> at scala.collection.AbstractTraversable.map(Traversable.scala:108)
> at org.apache.spark.sql.catalyst.optimizer.ConvertToLocalRelation$$anonfun$apply$46.applyOrElse(Optimizer.scala:1994)
> at org.apache.spark.sql.catalyst.optimizer.ConvertToLocalRelation$$anonfun$apply$46.applyOrElse(Optimizer.scala:1989)
> at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDownWithPruning$1(TreeNode.scala:584)
> at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:176)
> at org.apache.spark.sql.catalyst.trees.TreeNode.transformDownWithPruning(TreeNode.scala:584)
> at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.org$apache$spark$sql$catalyst$plans$logical$AnalysisHelper$$super$transformDownWithPruning(LogicalPlan.scala:30)
> at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning(AnalysisHelper.scala:267)
> at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning$(AnalysisHelper.scala:263)
> at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:30)
> at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:30)
> at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDownWithPruning$3(TreeNode.scala:589)
> at org.apache.spark.sql.catalyst.trees.UnaryLike.mapChildren(TreeNode.scala:1228)
> at org.apache.spark.sql.catalyst.trees.UnaryLike.mapChildren$(TreeNode.scala:1227)
> at org.apache.spark.sql.hive.execution.InsertIntoHiveTable.mapChildren(InsertIntoHiveTable.scala:73)
> at org.apache.spark.sql.catalyst.trees.TreeNode.transformDownWithPruning(TreeNode.scala:589)
> at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.org$apache$spark$sql$catalyst$plans$logical$AnalysisHelper$$super$transformDownWithPruning(LogicalPlan.scala:30)
> at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning(AnalysisHelper.scala:267)
> at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning$(AnalysisHelper.scala:263)
> at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:30)
> at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:30)
> at org.apache.spark.sql.catalyst.trees.TreeNode.transformWithPruning(TreeNode.scala:550)
> at org.apache.spark.sql.catalyst.optimizer.ConvertToLocalRelation$.apply(Optimizer.scala:1989)
> at org.apache.spark.sql.catalyst.optimizer.ConvertToLocalRelation$.apply(Optimizer.scala:1987)
> at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$2(RuleExecutor.scala:211)
> at scala.collection.IndexedSeqOptimized.foldLeft(IndexedSeqOptimized.scala:60)
> at scala.collection.IndexedSeqOptimized.foldLeft$(IndexedSeqOptimized.scala:68)
> at scala.collection.mutable.WrappedArray.foldLeft(WrappedArray.scala:38)
> at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1(RuleExecutor.scala:208)
> at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1$adapted(RuleExecutor.scala:200)
> at scala.collection.immutable.List.foreach(List.scala:431)
> at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:200)
> at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$executeAndTrack$1(RuleExecutor.scala:179)
> at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:88)
> at org.apache.spark.sql.catalyst.rules.RuleExecutor.executeAndTrack(RuleExecutor.scala:179)
> at org.apache.spark.sql.execution.QueryExecution.$anonfun$optimizedPlan$1(QueryExecution.scala:126)
> at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
> at org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$2(QueryExecution.scala:185)
> at org.apache.spark.sql.execution.QueryExecution$.withInternalError(QueryExecution.scala:510)
> at org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$1(QueryExecution.scala:185)
> at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:779)
> at org.apache.spark.sql.execution.QueryExecution.executePhase(QueryExecution.scala:184)
> at org.apache.spark.sql.execution.QueryExecution.optimizedPlan$lzycompute(QueryExecution.scala:122)
> at org.apache.spark.sql.execution.QueryExecution.optimizedPlan(QueryExecution.scala:118)
> at org.apache.spark.sql.execution.QueryExecution.assertOptimized(QueryExecution.scala:136)
> at org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:154)
> at org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:151)
> at org.apache.spark.sql.execution.QueryExecution.simpleString(QueryExecution.scala:204)
> at org.apache.spark.sql.execution.QueryExecution.org$apache$spark$sql$execution$QueryExecution$$explainString(QueryExecution.scala:249)
> at org.apache.spark.sql.execution.QueryExecution.explainString(QueryExecution.scala:218)
> at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$6(SQLExecution.scala:103)
> at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:169)
> at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:95)
> at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:779)
> at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
> at org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.applyOrElse(QueryExecution.scala:98)
> at org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.applyOrElse(QueryExecution.scala:94)
> at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDownWithPruning$1(TreeNode.scala:584)
> at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:176)
> at org.apache.spark.sql.catalyst.trees.TreeNode.transformDownWithPruning(TreeNode.scala:584)
> at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.org$apache$spark$sql$catalyst$plans$logical$AnalysisHelper$$super$transformDownWithPruning(LogicalPlan.scala:30)
> at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning(AnalysisHelper.scala:267)
> at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning$(AnalysisHelper.scala:263)
> at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:30)
> at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:30)
> at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:560)
> at org.apache.spark.sql.execution.QueryExecution.eagerlyExecuteCommands(QueryExecution.scala:94)
> at org.apache.spark.sql.execution.QueryExecution.commandExecuted$lzycompute(QueryExecution.scala:81)
> at org.apache.spark.sql.execution.QueryExecution.commandExecuted(QueryExecution.scala:79)
> at org.apache.spark.sql.Dataset.<init>(Dataset.scala:220)
> at org.apache.spark.sql.Dataset$.$anonfun$ofRows$2(Dataset.scala:100)
> at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:779)
> at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:97)
> at org.apache.spark.sql.SparkSession.$anonfun$sql$1(SparkSession.scala:622)
> at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:779)
> at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:617)
> ... 47 elided
> {noformat}
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