How can I achieve the following by passing a row to a udf ?
val df1 = df.withColumn("col_Z",
when($"col_x" === "a", $"col_A")
.when($"col_x" === "b", $"col_B")
.when($"col_x" === "c", $"col_C")
.when($"col_x" === "d", $"col_D")
.when($"col_x" === "e", $"col_E")
.when($"col_x" === "f", $"col_F")
.when($"col_x" === "g", $"col_G")
)
As I understand it, only columns can be passed as arguments to a UDF in Scala
Spark.
I have taken a look at this question:
https://stackoverflow.com/questions/31816975/how-to-pass-whole-row-to-udf-spark-dataframe-filter
and tried to implement this udf:
def myUDF(r:Row) = udf {
val z : Float = r.getAs("col_x") match {
case "a" => r.getAs("col_A")
case "b" => r.getAs("col_B")
case other => lit(0.0)
}
z
}
but I'm getting a type mismatch error:
error: type mismatch;
found : String("a")
required: Nothing
case "a" => r.getAs("col_A")
^
What am I doing wrong ?
Sent from my iPhone
