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

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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.

apache-spark-sql apache-spark scala
1 Answers

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...

user_6470530
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