Evaluate formulas in Spark DataFrame

Question

Is it possible to evaluate formulas in a dataframe which refer to columns? e.g. if I have data like this (Scala example):

val df = Seq(
    ( 1, "(a+b)/d", 1, 20, 2, 3, 1 ),
    ( 2, "(c+b)*(a+e)", 0, 1, 2, 3, 4 ),
    ( 3, "a*(d+e+c)", 7, 10, 6, 2, 1 )
  )
    .toDF( "Id", "formula", "a", "b", "c", "d", "e" )

df.show()

Expected results:

I have been unable to get selectExpr, expr, eval() or combinations of them to work.

Answer 1

You can use the scala toolbox eval in a UDF:

import org.apache.spark.sql.functions.col
import scala.reflect.runtime.universe
import scala.tools.reflect.ToolBox
val tb = universe.runtimeMirror(getClass.getClassLoader).mkToolBox()

val cols = df.columns.tail
val eval_udf = udf(
    (r: Seq[String]) => 
    tb.eval(tb.parse(
        ("val %s = %s;" * cols.tail.size).format(
            cols.tail.zip(r.tail).flatMap(x => List(x._1, x._2)): _*
        ) + r(0)
    )).toString
)

val df2 = df.select(col("id"), eval_udf(array(df.columns.tail.map(col):_*)).as("result"))

df2.show
+---+------+
| id|result|
+---+------+
|  1|     7|
|  2|    12|
|  3|    63|
+---+------+

Answer 2

A slightly different version of mck's answer, by replacing the variables in the formula column by their corresponding values from the other columns then calling eval udf :

import scala.reflect.runtime.currentMirror
import scala.tools.reflect.ToolBox

val eval = udf((f: String) => {
  val toolbox = currentMirror.mkToolBox()
  toolbox.eval(toolbox.parse(f)).toString
})


val formulaExpr = expr(df.columns.drop(2).foldLeft("formula")((acc, c) => s"replace($acc, '$c', $c)"))    
df.select($"Id", eval(formulaExpr).as("result")).show()

//+---+------+
//| Id|result|
//+---+------+
//|  1|     7|
//|  2|    12|
//|  3|    63|
//+---+------+