Perform lookup on a broadcasted Map conditoned on column value in Spark using Scala - scala

Question

I want to perform a lookup on myMap. When col2 value is "0000" I want to update it with the value related to col1 key. Otherwise I want to keep the existing col2 value.
val myDF :
+-----+-----+
|col1 |col2 |
+-----+-----+
|1 |a |
|2 |0000 |
|3 |c |
|4 |0000 |
+-----+-----+
val myMap : Map[String, String] ("2" -> "b", "4" -> "d")
val broadcastMyMap = spark.sparkContext.broadcast(myMap)
def lookup = udf((key:String) => broadcastMyMap.value.get(key))
myDF.withColumn("col2", when ($"col2" === "0000", lookup($"col1")).otherwise($"col2"))
I've used the code above in spark-shell and it works fine but when I build the application jar and submit it to Spark using spark-submit it throws an error:
org.apache.spark.SparkException: Failed to execute user defined function(anonfun$5: (string) => string)
Caused by: java.lang.NullPointerException
Is there a way to perform the lookup without using UDF, which aren't the best option in terms of performance, or to fix the error?
I think I can't just use join because some values of myDF.col2 that have to be kept could be sobstituted in the operation.

Answer 1

your NullPointerException is NOT Valid.I proved with sample program like below.
its PERFECTLY WORKING FINE. you execute the below program.
package com.example
import org.apache.log4j.{Level, Logger}
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.expressions.UserDefinedFunction
object MapLookupDF {
Logger.getLogger("org").setLevel(Level.OFF)
def main(args: Array[String]) {
import org.apache.spark.sql.functions._
val spark = SparkSession.builder.
master("local[*]")
.appName("MapLookupDF")
.getOrCreate()
import spark.implicits._
val mydf = Seq((1, "a"), (2, "0000"), (3, "c"), (4, "0000")).toDF("col1", "col2")
mydf.show
val myMap: Map[String, String] = Map("2" -> "b", "4" -> "d")
println(myMap.toString)
val broadcastMyMap = spark.sparkContext.broadcast(myMap)
def lookup: UserDefinedFunction = udf((key: String) => {
println("getting the value for the key " + key)
broadcastMyMap.value.get(key)
}
)
val finaldf = mydf.withColumn("col2", when($"col2" === "0000", lookup($"col1")).otherwise($"col2"))
finaldf.show
}
}
Result :
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
+----+----+
|col1|col2|
+----+----+
| 1| a|
| 2|0000|
| 3| c|
| 4|0000|
+----+----+
Map(2 -> b, 4 -> d)
getting the value for the key 2
getting the value for the key 4
+----+----+
|col1|col2|
+----+----+
| 1| a|
| 2| b|
| 3| c|
| 4| d|
+----+----+
note: there wont be significant degradation for a small map broadcasted.
if you want to go with a dataframe you can go as convert map to dataframe
val df = myMap.toSeq.toDF("key", "val")
Map(2 -> b, 4 -> d) in dataframe format will be like
+----+----+
|key|val |
+----+----+
| 2| b|
| 4| d|
+----+----+
and then join like this
DIY...