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SPARK_VERSION = 2.2.0
我在尝试做 filter
时遇到了一个有趣的问题。在具有使用 UDF 添加的列的数据帧上。我能够用较小的数据集复制问题。
鉴于虚拟案例类:
case class Info(number: Int, color: String)
case class Record(name: String, infos: Seq[Info])
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))
val left = Seq(a, b).toDF
val right = Seq(a2, c, d).toDF
full_outer
加入这些数据帧加入,只拿右边的
val rightOnlyInfos = left.alias("l")
.join(right.alias("r"), Seq("name"), "full_outer")
.filter("l.infos is null")
.select($"name", $"r.infos".as("r_infos"))
rightOnlyInfos.show(false)
+----+-----------------------+
|name|r_infos |
+----+-----------------------+
|c |[[5,white], [4,orange]]|
|d |[[4,orange], [2,black]]|
+----+-----------------------+
r_infos
之一。包含颜色
black
def hasBlack = (s: Seq[Row]) => {
s.exists{ case Row(num: Int, color: String) =>
color == "black"
}
}
val joinedBreakdown = rightOnlyInfos.withColumn("has_black", udf(hasBlack).apply($"r_infos"))
joinedBreakdown.show(false)
+----+-----------------------+---------+
|name|r_infos |has_black|
+----+-----------------------+---------+
|c |[[5,white], [4,orange]]|false |
|d |[[4,orange], [2,black]]|true |
+----+-----------------------+---------+
joinedBreakdown.printSchema
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)
joinedBreakdown.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$2.apply(ScalaUDF.scala:91)
at org.apache.spark.sql.catalyst.expressions.ScalaUDF.eval(ScalaUDF.scala:1072)
... 114 more
最佳答案
这个答案没有解决问题存在的原因,而是我找到的解决方案。
我遇到了一个完全像这样的问题。我不确定原因,但我有两种解决方法对我有用。比我更聪明的人可能会向你解释这一切,但这里是我对问题的解决方案。
第一个解决方案
Spark 的行为就像该列尚不存在。可能是因为某种过滤器下推。强制 Spark 在过滤之前缓存结果。这使得该列“存在”。
val joinedBreakdown = rightOnlyInfos.withColumn("has_black", hasBlack($"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
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]
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)
== 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 |
+----+-----------------------+---------+
关于scala - 在使用该 UDF 的列上添加过滤器时,Spark Sql UDF 抛出 NullPointer,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/48066858/
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