scala – 在使用该UDF的列上添加过滤器时,Spark Sql UDF抛出NullPointer

2023年1月22日 291次阅读

SPARK_VERSION = 2.2.0

在尝试对具有使用UDF添加的列的数据框进行过滤时,我遇到了一个有趣的问题.我可以使用较小的数据集来复制问题.

鉴于虚拟案例类：

case class Info(number: Int, color: String)
case class Record(name: String, infos: Seq[Info])

以及以下数据：

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))

请执行下列操作…

创建两个数据帧(我们将左右调用它们)

val left = Seq(a, b).toDF
val right = Seq(a2, c, d).toDF

使用full_outer连接加入这些数据帧,并仅使用右侧的数据帧

val rightOnlyInfos  = left.alias("l")
  .join(right.alias("r"), Seq("name"), "full_outer")
  .filter("l.infos is null")
  .select($"name", $"r.infos".as("r_infos"))

这导致以下结果：

rightOnlyInfos.show(false)
+----+-----------------------+
|name|r_infos                |
+----+-----------------------+
|c   |[[5,white], [4,orange]]|
|d   |[[4,orange], [2,black]]|
+----+-----------------------+

使用以下udf,添加一个布尔值的新列,并表示其中一个r_infos是否包含黑色

def hasBlack = (s: Seq[Row]) => {
  s.exists{ case Row(num: Int, color: String) =>
    color == "black"
  }
}

val joinedBreakdown = rightOnlyInfos.withColumn("has_black", udf(hasBlack).apply($"r_infos"))

这是我现在遇到问题的地方.如果我执行以下操作,则不会出现任何错误：

joinedBreakdown.show(false)

它的结果(如预期的那样)：

+----+-----------------------+---------+
|name|r_infos                |has_black|
+----+-----------------------+---------+
|c   |[[5,white], [4,orange]]|false    |
|d   |[[4,orange], [2,black]]|true     |
+----+-----------------------+---------+

和架构

joinedBreakdown.printSchema

节目

root 
  |-- name: string (nullable = true) 
  |-- r_infos: array (nullable = true) 
  | |-- element: struct (containsNull = true)
  | | |-- number: integer (nullable = false) 
  | | |-- color: string (nullable = true)
  |-- has_black: boolean (nullable = true)

但是,当我尝试按结果过滤时,我收到一个错误：

joinedBreakdown.filter("has_black == true").show(false)

出现以下错误：

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

编辑：打开了一个jira问题.粘贴在此处用于跟踪目的：
https://issues.apache.org/jira/browse/SPARK-22942

最佳答案这个答案并没有解决为什么存在这个问题,而是我找到的解决方案.

我遇到了一个完全像这样的问题.我不确定原因,但我有两个对我有用的解决方法.比我聪明的人可能会向你解释一切,但这是我解决问题的方法.

第一个解决方案

Spark就像列不存在一样.可能是因为某种滤波器下推.强制Spark在过滤之前缓存结果.这使得列“存在”.

val joinedBreakdown = rightOnlyInfos.withColumn("has_black", hasBlack($"r_infos")).cache()
println(joinedBreakdown.count()) //This will force cache the results from after the UDF has been applied.
joinedBreakdown.filter("has_black == true").show(false)
joinedBreakdown.filter("has_black == true").explain

OUTPUT

2
+----+-----------------------+---------+
|name|r_infos                |has_black|
+----+-----------------------+---------+
|d   |[[4,orange], [2,black]]|true     |
+----+-----------------------+---------+
== Physical Plan ==
*Filter (has_black#112632 = true)
+- InMemoryTableScan [name#112622, r_infos#112628, has_black#112632], [(has_black#112632 = true)]
      +- InMemoryRelation [name#112622, r_infos#112628, has_black#112632], true, 10000, StorageLevel(disk, memory, deserialized, 1 replicas)
            +- *Project [coalesce(name#112606, name#112614) AS name#112622, infos#112615 AS r_infos#112628, UDF(infos#112615) AS has_black#112632]
               +- *Filter isnull(infos#112607)
                  +- SortMergeJoin [name#112606], [name#112614], FullOuter
                     :- *Sort [name#112606 ASC NULLS FIRST], false, 0
                     :  +- Exchange hashpartitioning(name#112606, 200)
                     :     +- LocalTableScan [name#112606, infos#112607]
                     +- *Sort [name#112614 ASC NULLS FIRST], false, 0
                        +- Exchange hashpartitioning(name#112614, 200)
                           +- LocalTableScan [name#112614, infos#112615]

第二个解决方案

不知道为什么这个工作,但你做了同样的事情,除了在UDF中放一个try / catch.在我被大吼之前,请知道使用try / catch控制流程是一种模式.要了解更多信息,我建议使用此question and answer.注意：我稍微编辑了您的UDF,使其看起来像我更熟悉的东西.

def hasBlack = udf((s: Seq[Row]) => {
  try{
    s.exists{ case Row(num: Int, color: String) =>
      color == "black"
    }
  } catch {
    case ex: Exception => false
  }
})

val joinedBreakdown = rightOnlyInfos.withColumn("has_black", hasBlack($"r_infos"))
joinedBreakdown.filter("has_black == true").explain
joinedBreakdown.filter("has_black == true").show(false)

OUTPUT

== Physical Plan ==
*Project [coalesce(name#112565, name#112573) AS name#112581, infos#112574 AS r_infos#112587, UDF(infos#112574) AS has_black#112591]
+- *Filter isnull(infos#112566)
   +- *BroadcastHashJoin [name#112565], [name#112573], RightOuter, BuildLeft, false
      :- BroadcastExchange HashedRelationBroadcastMode(ArrayBuffer(input[0, string, false]))
      :  +- *Filter isnotnull(name#112565)
      :     +- LocalTableScan [name#112565, infos#112566]
      +- *Filter (UDF(infos#112574) = true)
         +- LocalTableScan [name#112573, infos#112574]
+----+-----------------------+---------+
|name|r_infos                |has_black|
+----+-----------------------+---------+
|d   |[[4,orange], [2,black]]|true     |
+----+-----------------------+---------+

您可以看到查询计划是不同的,因为我在过滤器之前强制应用UDF.