Spark job completes without executing udf - scala

Question

I've been having an issue with a long, complicated spark job which contains a udf.
The issue I've been having is that the udf doesn't seem to get called properly, although there is no error message.
I know it isn't called properly because the output gets written, only anything the udf was supposed to calculate is NULL, and no print statements appear when debugging locally.
The only lead is that this code previously worked using different input data, meaning the error must have something to do with the input.
The change in inputs mostly means different column names are used, which is addressed in the code.
Print statements are executed given the first, 'working' input.
Both inputs are created using the same series of steps from the same database, and by inspection there doesn't appear to be a problem with either one.
I've never experienced this sort of behaviour before, and any leads on what might cause it would be appreciated.
The code is monolithic and inflexible - I'm working on refactoring, but it's not an easy piece to break apart. This is a short version of what is happening:
package mypackage
import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions._
import org.apache.spark.util._
import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
import org.apache.spark.sql.types._
import scala.collection.{Map => SMap}
object MyObject {
def main(args: Array[String]){
val spark: SparkSession = SparkSession.builder()
.appName("my app")
.config("spark.master", "local")
.getOrCreate()
import spark.implicits._
val bigInput = spark.read.parquet("inputname.parquet")
val reference_table = spark.read.parquet("reference_table.parquet")
val exchange_rate = spark.read.parquet("reference_table.parquet")
val bigInput2 = bigInput
.filter($"column1" === "condition1")
.join(joinargs)
.drop(dropargs)
val bigInput3 = bigInput
.filter($"column2" === "condition2")
.join(joinargs)
.drop(dropargs)
<continue for many lines...>
def mapper1(
arg1: String,
arg2: Double,
arg3: Integer
): List[Double]{
exchange_rate.map(
List(idx1, idx2, idx3),
r.toSeq.toList
.drop(idx4)
.take(arg2)
)
}
def mapper2(){}
...
def mapper5(){}
def my_udf(
arg0: Integer,
arg1: String,
arg2: Double,
arg3: Integer,
...
arg20: String
): Double = {
println("I'm actually doing something!")
val result1 = mapper1(arg1, arg2, arg3)
val result2 = mapper2(arg4, arg5, arg6, arg7)
...
val result5 = mapper5(arg18, arg19, arg20)
result1.take(arg0)
.zipAll(result1, 0.0, 0.0)
.map(x=>_1*x._2)
....
.zipAll(result5, 0.0, 0.0)
.foldLeft(0.0)(_+_)
}
spark.udf.register("myUDF", my_udf_)
val bigResult1 = bigInputFinal.withColumn("Newcolumnname",
callUDF(
"myUDF",
$"col1",
...
$"col20"
)
)
<postprocessing>
bigResultFinal
.filter(<configs>)
.select(<column names>)
.write
.format("parquet")
}
}
To recap
This code runs to completion on each of two input files.
The udf only appears to execute on the first file.
There are no error messages or anything using the second file, although all non-udf logic appears to complete successfully.
Any help greatly appreciated!

Answer 1

Here the UDF is not being called because spark is
Lazy it does not call the UDF unless you use any action on the dataframe. You can achieve this by forcing dataframe actions.