Exploding nested Struct in Spark dataframe - scala

Question

I'm working through a Databricks example. The schema for the dataframe looks like:
> parquetDF.printSchema
root
|-- department: struct (nullable = true)
| |-- id: string (nullable = true)
| |-- name: string (nullable = true)
|-- employees: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- firstName: string (nullable = true)
| | |-- lastName: string (nullable = true)
| | |-- email: string (nullable = true)
| | |-- salary: integer (nullable = true)
In the example, they show how to explode the employees column into 4 additional columns:
val explodeDF = parquetDF.explode($"employees") {
case Row(employee: Seq[Row]) => employee.map{ employee =>
val firstName = employee(0).asInstanceOf[String]
val lastName = employee(1).asInstanceOf[String]
val email = employee(2).asInstanceOf[String]
val salary = employee(3).asInstanceOf[Int]
Employee(firstName, lastName, email, salary)
}
}.cache()
display(explodeDF)
How would I do something similar with the department column (i.e. add two additional columns to the dataframe called "id" and "name")? The methods aren't exactly the same, and I can only figure out how to create a brand new data frame using:
val explodeDF = parquetDF.select("department.id","department.name")
display(explodeDF)
If I try:
val explodeDF = parquetDF.explode($"department") {
case Row(dept: Seq[String]) => dept.map{dept =>
val id = dept(0)
val name = dept(1)
}
}.cache()
display(explodeDF)
I get the warning and error:
<console>:38: warning: non-variable type argument String in type pattern Seq[String] is unchecked since it is eliminated by erasure
case Row(dept: Seq[String]) => dept.map{dept =>
^
<console>:37: error: inferred type arguments [Unit] do not conform to method explode's type parameter bounds [A <: Product]
val explodeDF = parquetDF.explode($"department") {
^

Answer 1

In my opinion the most elegant solution is to star expand a Struct using a select operator as shown below:
var explodedDf2 = explodedDf.select("department.*","*")
https://docs.databricks.com/spark/latest/spark-sql/complex-types.html

Answer 2

You could use something like that:
var explodeDF = explodeDF.withColumn("id", explodeDF("department.id"))
explodeDeptDF = explodeDeptDF.withColumn("name", explodeDeptDF("department.name"))
which you helped me into and these questions:
Flattening Rows in Spark
Spark 1.4.1 DataFrame explode list of JSON objects

Answer 3

This seems to work (though maybe not the most elegant solution).
var explodeDF2 = explodeDF.withColumn("id", explodeDF("department.id"))
explodeDF2 = explodeDF2.withColumn("name", explodeDF2("department.name"))