How to pass DataSet(s) to a function that accepts DataFrame(s) as arguments in Apache Spark using Scala?

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I have a library in Scala for Spark which contains many functions. One example is the following function to unite two dataframes that have different columns:

def appendDF(df2: DataFrame): DataFrame = {

  val cols1 = df.columns.toSeq
  val cols2 = df2.columns.toSeq

  def expr(sourceCols: Seq[String], targetCols: Seq[String]): Seq[Column] = {
    targetCols.map({
      case x if sourceCols.contains(x) => col(x)
      case y                           => lit(null).as(y)
    })
  }

  // both df's need to pass through `expr` to guarantee the same order, as needed for correct unions.
  df.select(expr(cols1, cols1): _*).union(df2.select(expr(cols2, cols1): _*))

}

I would like to use this function (and many more) to Dataset[CleanRow] and not DataFrames. CleanRow is a simple class here that defines the names and types of the columns. My educated guess is to convert the Dataset into Dataframe using .toDF() method. However, I would like to know whether there are better ways to do it.

From my understanding, there shouldn't be many differences between Dataset and Dataframe since Dataset are just Dataframe[Row]. Plus, I think that from Spark 2.x the APIs for DF and DS have been unified, so I was thinking that I could pass either of them interchangeably, but that's not the case.

apache-spark-dataset apache-spark-sql apache-spark scala
1 Answers

If changing signature is possible:

import spark.implicits._
import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.Dataset

def f[T](d: Dataset[T]): Dataset[T] = {d}

// You are able to pass a dataframe:
f(Seq(0,1).toDF()).show
// res1: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [value: int]

// You are also able to pass a dataset:
f(spark.createDataset(Seq(0,1)))
// res2: org.apache.spark.sql.Dataset[Int] = [value: int]
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