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https://issues.apache.org/jira/browse/SPARK-22942?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16308830#comment-16308830
]
Takeshi Yamamuro commented on SPARK-22942:
------------------------------------------
I think you just need NULL checks;
{code}
val hasBlack: Seq[Row] => Boolean = (s: Seq[Row]) => {
if (s != null) {
s.exists{ case Row(num: Int, color: String) =>
color == "black"
}
} else {
false
}
}
{code}
> Spark Sql UDF throwing NullPointer when adding a filter on a columns that
> uses that UDF
> ---------------------------------------------------------------------------------------
>
> Key: SPARK-22942
> URL: https://issues.apache.org/jira/browse/SPARK-22942
> Project: Spark
> Issue Type: Bug
> Components: Spark Shell, SQL
> Affects Versions: 2.2.0
> Reporter: Matthew Fishkin
>
> I ran into an interesting issue when trying to do a `filter` on a dataframe
> that has columns that were added using a UDF. I am able to replicate the
> problem with a smaller set of data.
> Given the dummy case classes:
> {code:java}
> case class Info(number: Int, color: String)
> case class Record(name: String, infos: Seq[Info])
> {code}
> And the following data:
> {code:java}
> val blue = Info(1, "blue")
> val black = Info(2, "black")
> val yellow = Info(3, "yellow")
> val orange = Info(4, "orange")
> val white = Info(5, "white")
> val a = Record("a", Seq(blue, black, white))
> val a2 = Record("a", Seq(yellow, white, orange))
> val b = Record("b", Seq(blue, black))
> val c = Record("c", Seq(white, orange))
> val d = Record("d", Seq(orange, black))
> {code}
> Create two dataframes (we will call them left and right)
> {code:java}
> val left = Seq(a, b).toDF
> val right = Seq(a2, c, d).toDF
> {code}
> Join those two dataframes with an outer join (So two of our columns are
> nullable now.
> {code:java}
> val joined = left.alias("l").join(right.alias("r"), Seq("name"), "full_outer")
> joined.show(false)
> res0:
> +----+--------------------------------+-----------------------------------+
> |name|infos |infos |
> +----+--------------------------------+-----------------------------------+
> |b |[[1,blue], [2,black]] |null |
> |a |[[1,blue], [2,black], [5,white]]|[[3,yellow], [5,white], [4,orange]]|
> |c |null |[[5,white], [4,orange]] |
> |d |null |[[4,orange], [2,black]] |
> +----+--------------------------------+-----------------------------------+
> {code}
> Then, take only the `name`s that exist in the right Dataframe
> {code:java}
> val rightOnly = joined.filter("l.infos is null").select($"name",
> $"r.infos".as("r_infos"))
> rightOnly.show(false)
> res1:
> +----+-----------------------+
> |name|r_infos |
> +----+-----------------------+
> |c |[[5,white], [4,orange]]|
> |d |[[4,orange], [2,black]]|
> +----+-----------------------+
> {code}
> Now, add a new column called `has_black` which will be true if the `r_infos`
> contains _black_ as a color
> {code:java}
> def hasBlack = (s: Seq[Row]) => {
> s.exists{ case Row(num: Int, color: String) =>
> color == "black"
> }
> }
> val rightBreakdown = rightOnly.withColumn("has_black",
> udf(hasBlack).apply($"r_infos"))
> rightBreakdown.show(false)
> res2:
> +----+-----------------------+---------+
> |name|r_infos |has_black|
> +----+-----------------------+---------+
> |c |[[5,white], [4,orange]]|false |
> |d |[[4,orange], [2,black]]|true |
> +----+-----------------------+---------+
> {code}
> So far, _exactly_ what we expected.
> *However*, when I try to filter `rightBreakdown`, it fails.
> {code:java}
> rightBreakdown.filter("has_black == true").show(false)
> org.apache.spark.SparkException: Failed to execute user defined
> function($anonfun$hasBlack$1: (array<struct<number:int,color:string>>) =>
> boolean)
> at
> org.apache.spark.sql.catalyst.expressions.ScalaUDF.eval(ScalaUDF.scala:1075)
> at
> org.apache.spark.sql.catalyst.expressions.BinaryExpression.eval(Expression.scala:411)
> at
> org.apache.spark.sql.catalyst.optimizer.EliminateOuterJoin.org$apache$spark$sql$catalyst$optimizer$EliminateOuterJoin$$canFilterOutNull(joins.scala:127)
> at
> org.apache.spark.sql.catalyst.optimizer.EliminateOuterJoin$$anonfun$rightHasNonNullPredicate$lzycompute$1$1.apply(joins.scala:138)
> at
> org.apache.spark.sql.catalyst.optimizer.EliminateOuterJoin$$anonfun$rightHasNonNullPredicate$lzycompute$1$1.apply(joins.scala:138)
> at
> scala.collection.LinearSeqOptimized$class.exists(LinearSeqOptimized.scala:93)
> at scala.collection.immutable.List.exists(List.scala:84)
> at
> org.apache.spark.sql.catalyst.optimizer.EliminateOuterJoin.rightHasNonNullPredicate$lzycompute$1(joins.scala:138)
> at
> org.apache.spark.sql.catalyst.optimizer.EliminateOuterJoin.rightHasNonNullPredicate$1(joins.scala:138)
> at
> org.apache.spark.sql.catalyst.optimizer.EliminateOuterJoin.org$apache$spark$sql$catalyst$optimizer$EliminateOuterJoin$$buildNewJoinType(joins.scala:145)
> at
> org.apache.spark.sql.catalyst.optimizer.EliminateOuterJoin$$anonfun$apply$2.applyOrElse(joins.scala:152)
> at
> org.apache.spark.sql.catalyst.optimizer.EliminateOuterJoin$$anonfun$apply$2.applyOrElse(joins.scala:150)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256)
> at
> org.apache.spark.sql.catalyst.optimizer.EliminateOuterJoin.apply(joins.scala:150)
> at
> org.apache.spark.sql.catalyst.optimizer.EliminateOuterJoin.apply(joins.scala:116)
> at
> org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:85)
> at
> org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:82)
> at
> scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:124)
> at scala.collection.immutable.List.foldLeft(List.scala:84)
> at
> org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:82)
> at
> org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:74)
> at scala.collection.immutable.List.foreach(List.scala:381)
> at
> org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:74)
> at
> org.apache.spark.sql.execution.QueryExecution.optimizedPlan$lzycompute(QueryExecution.scala:78)
> at
> org.apache.spark.sql.execution.QueryExecution.optimizedPlan(QueryExecution.scala:78)
> at
> org.apache.spark.sql.execution.QueryExecution.sparkPlan$lzycompute(QueryExecution.scala:84)
> at
> org.apache.spark.sql.execution.QueryExecution.sparkPlan(QueryExecution.scala:80)
> at
> org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:89)
> at
> org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:89)
> at org.apache.spark.sql.Dataset.withAction(Dataset.scala:2832)
> at org.apache.spark.sql.Dataset.head(Dataset.scala:2153)
> at org.apache.spark.sql.Dataset.take(Dataset.scala:2366)
> at org.apache.spark.sql.Dataset.showString(Dataset.scala:245)
> at org.apache.spark.sql.Dataset.show(Dataset.scala:646)
> at org.apache.spark.sql.Dataset.show(Dataset.scala:623)
> ... 58 elided
> Caused by: java.lang.NullPointerException
> at $anonfun$hasBlack$1.apply(<console>:41)
> at $anonfun$hasBlack$1.apply(<console>:40)
> at
> org.apache.spark.sql.catalyst.expressions.ScalaUDF$$anonfun$2.apply(ScalaUDF.scala:92)
> at
> org.apache.spark.sql.catalyst.expressions.ScalaUDF$$anonfun$2.apply(ScalaUDF.scala:91)
> at
> org.apache.spark.sql.catalyst.expressions.ScalaUDF.eval(ScalaUDF.scala:1072)
> ... 114 more
> {code}
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