scala spark UDF ClassCastException：WrappedArray$ofRef 无法转换为 [Lscala.Tuple2

Question

所以我执行必要的导入等

import org.apache.spark.sql.functions.udf
import org.apache.spark.sql.types._
import spark.implicits._

然后定义一些经纬度点

val london = (1.0, 1.0)
val suburbia = (2.0, 2.0)
val southampton = (3.0, 3.0)  
val york = (4.0, 4.0)  

然后我像这样创建一个 spark Dataframe 并检查它是否有效：

val exampleDF = Seq((List(london,suburbia),List(southampton,york)),
    (List(york,london),List(southampton,suburbia))).toDF("AR1","AR2")
exampleDF.show()

数据框由以下类型组成

DataFrame = [AR1: array<struct<_1:double,_2:double>>, AR2: array<struct<_1:double,_2:double>>]

我创建了一个函数来创建点的组合

// function to do what I want
val latlongexplode =  (x: Array[(Double,Double)], y: Array[(Double,Double)]) => {
 for (a <- x; b <-y) yield (a,b)
}

我检查该功能是否有效

latlongexplode(Array(london,york),Array(suburbia,southampton))

确实如此。但是，在我使用此函数创建 UDF 后

// declare function into a Spark UDF
val latlongexplodeUDF = udf (latlongexplode) 

当我尝试在上面创建的 spark 数据框中使用它时：

exampleDF.withColumn("latlongexplode", latlongexplodeUDF($"AR1",$"AR2")).show(false)

我得到了一个很长的堆栈跟踪，基本上可以归结为：

java.lang.ClassCastException： scala.collection.mutable.WrappedArray$ofRef 不能转换为 [Lscala.Tuple2;
org.apache.spark.sql.catalyst.expressions.ScalaUDF.$anonfun$f$3(ScalaUDF.scala:121) org.apache.spark.sql.catalyst.expressions.ScalaUDF.eval(ScalaUDF.scala:1063) org.apache.spark.sql.catalyst.expressions.Alias.eval(namedExpressions.scala:151) org.apache.spark.sql.catalyst.expressions.InterpretedProjection.apply(Projection.scala:50) org.apache.spark.sql.catalyst.expressions.InterpretedProjection.apply(Projection.scala:32) scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:27​​3)

如何让这个 udf 在 Scala Spark 中工作？ （如果这有帮助，我目前正在使用 2.4）

编辑：可能是我构建示例 df 的方式存在问题。 但我所拥有的实际数据是每列上的纬度/经度元组的数组（大小未知）。

Answer 1

在 UDF 中使用结构类型时，它们表示为 Row 对象，而数组列表示为 Seq。另外，你需要以Row的形式返回一个struct，并且你需要定义一个schema来返回一个struct。

import org.apache.spark.sql.Row
import org.apache.spark.sql.types._

val london = (1.0, 1.0)
val suburbia = (2.0, 2.0)
val southampton = (3.0, 3.0)  
val york = (4.0, 4.0)
val exampleDF = Seq((List(london,suburbia),List(southampton,york)),
    (List(york,london),List(southampton,suburbia))).toDF("AR1","AR2")
exampleDF.show(false)
+------------------------+------------------------+
|AR1                     |AR2                     |
+------------------------+------------------------+
|[[1.0, 1.0], [2.0, 2.0]]|[[3.0, 3.0], [4.0, 4.0]]|
|[[4.0, 4.0], [1.0, 1.0]]|[[3.0, 3.0], [2.0, 2.0]]|
+------------------------+------------------------+

val latlongexplode = (x: Seq[Row], y: Seq[Row]) => {
    for (a <- x; b <- y) yield Row(a, b)
}

val udf_schema = ArrayType(
    StructType(Seq(
        StructField(
            "city1",
            StructType(Seq(
                StructField("lat", FloatType),
                StructField("long", FloatType)
            ))
        ),
        StructField(
            "city2",
            StructType(Seq(
                StructField("lat", FloatType),
                StructField("long", FloatType)
            ))
        )
    ))
)

// include this line if you see errors like 
// "You're using untyped Scala UDF, which does not have the input type information."
// spark.sql("set spark.sql.legacy.allowUntypedScalaUDF = true")

val latlongexplodeUDF = udf(latlongexplode, udf_schema)
result = exampleDF.withColumn("latlongexplode", latlongexplodeUDF($"AR1",$"AR2"))

result.show(false)
+------------------------+------------------------+--------------------------------------------------------------------------------------------------------+
|AR1                     |AR2                     |latlongexplode                                                                                          |
+------------------------+------------------------+--------------------------------------------------------------------------------------------------------+
|[[1.0, 1.0], [2.0, 2.0]]|[[3.0, 3.0], [4.0, 4.0]]|[[[1.0, 1.0], [3.0, 3.0]], [[1.0, 1.0], [4.0, 4.0]], [[2.0, 2.0], [3.0, 3.0]], [[2.0, 2.0], [4.0, 4.0]]]|
|[[4.0, 4.0], [1.0, 1.0]]|[[3.0, 3.0], [2.0, 2.0]]|[[[4.0, 4.0], [3.0, 3.0]], [[4.0, 4.0], [2.0, 2.0]], [[1.0, 1.0], [3.0, 3.0]], [[1.0, 1.0], [2.0, 2.0]]]|
+------------------------+------------------------+--------------------------------------------------------------------------------------------------------+