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https://issues.apache.org/jira/browse/SPARK-40316?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17599644#comment-17599644
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Sachit edited comment on SPARK-40316 at 9/2/22 5:30 PM:
Hi [~srowen]
I am using UDF in scala which is causing this. To debug tried changing data
type from Seq[Long] to List[Long]
Even tried changing to Option[Seq[Long]].
Here is signature of UDF
val myUDF: UserDefinedFunction = udf((inputList: Seq[Long], newList: Seq[Long],
n: Int, p: Int) => {
}
was (Author: JIRAUSER287754):
I am using UDF in scala which is causing this. To debug tried changing data
type from Seq[Long] to List[Long]
Even tried changing to Option[Seq[Long]].
Here is signature of UDF
val myUDF: UserDefinedFunction = udf((inputList: Seq[Long], newList: Seq[Long],
n: Int, p: Int) => {
}
> Upgrading to Spark 3 is giving NullPointerException
> ---
>
> Key: SPARK-40316
> URL: https://issues.apache.org/jira/browse/SPARK-40316
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
>Affects Versions: 3.2.2
>Reporter: Sachit
>Priority: Major
>
> Getting below error while upgrading to Spark3
>
> java.lang.RuntimeException: Error while decoding:
> java.lang.NullPointerException: Null value appeared in non-nullable field:
> - array element class: "scala.Long"
> - root class: "scala.collection.Seq"
> If the schema is inferred from a Scala tuple/case class, or a Java bean,
> please try to use scala.Option[_] or other nullable types (e.g.
> java.lang.Integer instead of int/scala.Int).
> mapobjects(lambdavariable(MapObject, LongType, true, -1),
> assertnotnull(lambdavariable(MapObject, LongType, true, -1)), input[0,
> array, true], Some(interface scala.collection.Seq))
> at
> org.apache.spark.sql.errors.QueryExecutionErrors$.expressionDecodingError(QueryExecutionErrors.scala:1047)
> at
> org.apache.spark.sql.catalyst.encoders.ExpressionEncoder$Deserializer.apply(ExpressionEncoder.scala:184)
> at
> org.apache.spark.sql.catalyst.expressions.ScalaUDF.$anonfun$scalaConverter$2(ScalaUDF.scala:164)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage3.processNext(Unknown
> Source)
> at
> org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at
> org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:759)
> at
> org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:350)
> at
> org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:898)
> at
> org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:898)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
> at org.apache.spark.scheduler.Task.run(Task.scala:131)
> at
> org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:506)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1491)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:509)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
> at java.lang.Thread.run(Thread.java:748)
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